research topics in computer science education

• Open access
• Published: 06 September 2023

Game-based learning in computer science education: a scoping literature review

• Maja Videnovik   ORCID: orcid.org/0000-0002-9859-5051 1 ,
• Tone Vold   ORCID: orcid.org/0000-0003-4850-3363 2 ,
• Linda Kiønig   ORCID: orcid.org/0000-0001-8768-9370 2 ,
• Ana Madevska Bogdanova   ORCID: orcid.org/0000-0002-0906-3548 3 &
• Vladimir Trajkovik   ORCID: orcid.org/0000-0001-8103-8059 3  

International Journal of STEM Education volume  10 , Article number:  54 ( 2023 ) Cite this article

13k Accesses

3 Citations

2 Altmetric

Metrics details

Using games in education has the potential to increase students’ motivation and engagement in the learning process, gathering long-lasting practical knowledge. Expanding interest in implementing a game-based approach in computer science education highlights the need for a comprehensive overview of the literature research. This scoping review aims to provide insight into current trends and identify research gaps and potential research topics concerning game-based learning in computer science. Using standard methodology for scoping review, we identified 113 articles from four digital libraries published between 2017 and 2021. Those articles were analyzed concerning the educational level, type of the game, computer science topic covered by the game, pedagogical strategies, and purpose for implementing this approach in different educational levels. The results show that the number of research articles has increased through the years, confirming the importance of implementing a game-based approach in computer science. Different kinds of games, using different technology, concerning different computer science topics are presented in the research. The obtained results indicate that there is no standardized game or standardized methodology that can be used for the creation of an educational game for computer science education. Analyzed articles mainly implement a game-based approach using learning by playing, and no significant focus is given to the effectiveness of learning by designing a game as a pedagogical strategy. Moreover, the approach is mainly implemented for developing computational thinking or programming skills, highlighting the need for its implementation in other topics beyond programming.

Introduction

The world is changing very fast due to the emergence of technology in our everyday lives. This tremendous change can be noticed in different areas, including education. Students are influenced by the digital era, surrounded by technology and working with a massive amount of digital information on an everyday base. They are used to interactive environments and fast communication and prefer learning by doing (Unger & Meiran, 2020 ). Traditional learning environments, where students should sit and listen to the information provided by the teachers are unacceptable for them (Campbell, 2020 ). Students require active learning environments, using the possibilities of various technology applications to gain knowledge. They seek more interesting, fun, motivating and engaging learning experiences (Anastasiadis et al., 2018 ).

Creating engaging learning environments can develop students' critical thinking, problem-solving skills, creativity and cooperation, preparing students for living in a constantly changing world (Joshi et al., 2022 ; Lapek, 2018 ; Tang et al., 2020 ). Education needs to shift toward active learning approaches that will encourage students to engage on a deeper level than traditional lecture-based methods (Boyer et al., 2014 ). To achieve this, teachers must find an approach tied to digital tools that students use daily (Videnovik et al., 2020 ).

Implementation of a game-based learning approach for creating engaging learning environments

Game-based learning is considered one of the most innovative learning approaches for increasing students' interest in education by playing games (Priyaadharshini et al., 2020 ). It refers to using games as an educational tool or strategy to facilitate learning and engagement (Li et al., 2021 ). Game-based learning involves designing and incorporating educational content within a game format, where players actively participate and interact with the game mechanics to acquire knowledge or develop skills. Many approaches tackle the umbrella of application of game-based learning in different educational fields. Different playful experiences can enable children to construct knowledge by playing and exploring a real-world problem often driven by students’ interest in inquiry (Hirsh-Pasek, 2020 ). Gamification is a process that uses game elements, such as points, rewards, badges and competition during the learning process, establishing interactive and engaging learning environments (Turan et al., 2016 ). Gamification aims to enhance motivation, engagement, and participation using the inherent appeal of games. Designing interactive and entertaining games, primarily for education, is a step forward in implementing game-based learning. Serious games enable players to cultivate their knowledge and practice their skills by overcoming numerous interruptions during gaming (Yu, 2019 ). Effectively designed serious games facilitate learning by stimulating creativity, igniting interest, promoting discourse, and cultivating a competitive drive for exploration in diverse fields. Different mobile and location-based technologies provide opportunities to embed learning in authentic environments and thereby enhance engagement and learning outside traditional formal educational settings (Huizenga et al., 2009 ). Those games can simulate various aspects of reality, such as driving a vehicle, managing a city, or piloting an aircraft, allowing players to experiment and make decisions in a safe space without real-world consequences (Toh & Kirschner, 2020 ).

Games enable the integration of intrinsic and extrinsic motivational components to create an environment, where players feel more motivated to engage in the activities (Hartt et al., 2020 ). When digital game-based learning is implemented, including key game design elements (collaboration, choice, feedback), there is typically a positive impact on student engagement (Serrano, 2019 ; Wang et al., 2022 ). Students approach gameplay with interest and dedication and are persistent in progressing it. Therefore, teachers must find different ways to implement a game-based approach in the classroom, utilizing students' engagement, persistence and motivation during gameplay for classroom activities. During game-based learning, students have fun and enjoy themselves with increased imagination and natural curiosity, which can lead to high levels of participation and the student's involvement in the learning process. In this way, students can be more successfully engaged in meaningful learning than traditional teaching methods (Hamari et al., 2016 ; Huizenga et al., 2009 ; Karram, 2021 ).

Research on using a game-based learning approach in education

In the last decade, the game-based approach is receiving increasing attention in the research community due to its potential to increase students' motivation and engagement, promoting a student-centred learning environment. Many researchers show that digital game-based learning is becoming a powerful tool in education, making learning more enjoyable, easier and efficient (Boyle et al., 2016 ; Hafeez, 2022 ). Implementation of a game-based learning approach can provide students with an engaging, motivating and stimulating environment (Ghergulescu & Muntean, 2012 ; Hwang et al., 2014 ), supporting them to focus on the task and increasing overall learning experiences (Hamari et al., 2016 ). Moreover, game-based learning has the potential to improve students’ competencies and academic performance (Clark et al., 2016 ; López-Fernández et al., 2021a , 2021b ; Mezentseva et al., 2021 ; Noroozi et al., 2020 ; Sanchez Mena & Martí-Parreño, 2017 ; Vu & Feinstein, 2017 ). It presents the learners with rich, immersive environments and experiences that are not just about learning facts but enables the development of problem-solving, decision-making, and strategic planning (Lymbery, 2012 ; Sung & Hwang, 2013 ) skills. In addition, the student's academic achievement using a game-based approach is better than those learning through the traditional method (Arcagök, 2021 ; Partovi & Razavi, 2019 ; Roodt & Ryklief, 2022 ; Wang et al., 2022 ). Educational games promote active and self-directed learning, enabling students to learn from authentic situations and receive immediate feedback (Pellas & Mystakidis, 2020 ; Zhao et al., 2021 ). It can be highly personalized, allowing students to learn at their own pace and in a way best suited to their individual needs and learning styles, engaging them in the self-assessment process (Videnovik et al., 2022 ). In a gaming environment, students can explore different scenarios, make choices, and learn from the consequences of their actions without fear of making a mistake.

Despite the great potential of the game-based approach for learning, it must be noted that developing educational games can be very complex and costly, and faces significant challenges (Boyle et al., 2016 ). The process of designing an educational game needs a lot of planning and requires a lot of skills (Hussein et al., 2019 ). Teachers do not have necessary skills to develop a game that combines entertainment and educational elements to increase student's interest and motivation during learning (Qian & Clarck, 2016 ). On the other side, game developers have problem to align educational goals within the game. In addition, the games must be well-designed and with the right level of complexity so the learners should not be bored or frustrated during the play (Liu et al., 2020 ; Vlahu-Gjorgievska et al., 2018 ), taking into account both educational and entertainment elements. That is why educators cannot depend solely on professional game designers and must take on the responsibility of creating these immersive learning experiences themselves or by engaging their students in the design process.

Game-based learning approach in computer science education

The game-based approach provides a dynamic and effective way for students to learn and apply their knowledge in a variety of subjects, such as math (Vankúš, 2021 ), physics (Cardinot & Fairfield, 2019 ), languages (Lee, 2019 ), and history (Kusuma et al., 2021 ). This approach allows students to learn complex concepts and skills in a fun and interactive way while also fostering critical thinking and collaboration. It is particularly effective in computer science, where students can learn about algorithms, data structures, networks, software testing and programming languages by designing and testing their games and simulations (Kalderova et al., 2023 ). In addition, game-based learning can help to bridge the gap between theory and practice, allowing students to apply their knowledge in a real-world context (Barz et al., 2023 ).

The importance of computer science has been emphasized in the last decade through different campaigns and online platforms. Their main aim is to develop students' computational thinking skills and attract students to coding, mainly through a game-based approach (code.org, codeweek.org). They offer teachers access to materials and learning scenarios covering different unplugged activities and block-based programming. Students have an opportunity to play games and learn basic programming concepts through fun and interactive activities, developing collaboration and competitiveness at the same time. Game narratives, collecting points, and immediate feedback through these games increase students’ engagement. These platforms are a valid option for developing computational thinking at an early age and a good way for students to develop creativity, critical thinking and problem-solving skills (Barradas et al., 2020 ).

Various block-based programming languages, which are also accessible online (Scratch, Footnote 1 Snap, Footnote 2 Blockly Footnote 3 ), are used to develop students' computational thinking and block-based programming skills, especially in primary education. In addition, they support the development of interactive projects that students can use afterward (Tsur & Rusk, 2018 ). Moreover, students can develop animations, interactive stories, and games, which allow them to engage in the coding process, learn programming concepts and even learn about other computer science topics during game design.

Topics connected with programming are the most common in computer science, but learning how to program is often recognized as a frustrating activity (Yassine et al., 2018 ). Learning object-oriented programming languages is especially difficult for students, because programming concepts are complex, cognitively demanding, require algorithmic thinking and problem-solving skills, and is a long-term process (Zapušek & Rugelj, 2013 ). Game-based learning stimulates active learning and enables students to learn about programming concepts in fun and engaging ways through visual interfaces and engaging environments (CodeCombat, Footnote 4 Alice, Footnote 5 Greenfoot Footnote 6 ). Those engaging and motivating environments enable simplifying complex programming concepts, such as inheritance, nested loops, and recursion (Karram, 2021 ).

Different pedagogical strategies can be used to implement game-based learning in computer science, empowering students' skills and increasing their active engagement in learning. For example, students can deepen their knowledge and skills on a given topic by playing the game (Hooshyar et al., 2021 ; Shabalina et al., 2017 ) or through the process of game design (Denner et al., 2012 ; Zhang et al., 2014 ). In both cases, the game-based approach can increase students' motivation and engagement in learning (Chandel et al., 2015 ; Park et al., 2020 ).

Existing reviews of game-based approach in computer science

Existing reviews of game-based approach in computer science provide valuable information about the latest trends in the implementation of game-based approach in the last few years. Table 1 presents latest trends in the implementation of game-based learning in computer science education.

Most of the review articles analyze publications that describe the implementation of game-based approach for learning programming (Abbasi et al., 2017 ; Diaz et al., 2021 ; Dos Santos et al., 2019 ; Laporte & Zaman, 2018 ; Shahid et al., 2019 ), from different aspects: game design, game elements, or their evaluation. However, there are some of them tackling other topics, such as cybersecurity (Karagiannis et al., 2020 ; Tioh et al., 2017 ) or cyberbullying (Calvo-Morata et al., 2020 ). Sharma et al. ( 2021 ) analyzes the impact of game-based learning on girls’ perception toward computer science. There are review articles that focus on just one aspect of computer science. For example, Chen et al. ( 2023 ) provides meta-analyses to investigate potential of unplugged activities on computational thinking skills.

In our review, we aim to perform the broader analysis of the research articles referring to the game-based approach in various computer science topics, different educational levels and different types of games. For that purpose, instead of systematic review, we have opted to perform the scoping review on significantly larger set of articles.

Valuable insight regarding the game-based approach in computer science has been provided in research concerning different educational levels, computer science topics, and used games. However, computer science is a field that is changing very fast, and the number of games that can be used for developing students' knowledge and skills is increasing all the time. As a result, continuous research in this field should be done.

This research aims to elaborate on current trends concerning the game-based approach in computer science. It focuses on the educational level, covered computer science topic, type of the game, purpose for its use, and pedagogical strategies for the implementation of this approach. Moreover, possible gaps and potential research topics concerning game-based learning in computer science in primary education are identified.

Current review

This research represents scoping review that identifies the educational context and the type of games used for implementing a game-based learning approach in computer science. The scoping review method was selected over systematic literature review, because we wanted to determine the scope of the literature in the field of game-based learning in computer science education, to examine how research is done on this topic and to identify and analyze research gaps in the literature (Munn et al., 2018 ).

Following Arksey and O’Malley ( 2005 ) five-step framework, which adopts a rigorous process of transparency, enabling replication of the search method and increasing the reliability of the results, the steps of the applied review process are: to (1) identify research questions (2) identify relevant studies, (3) study selection of papers, (4) charting the data, (5) summarizing and reporting the results.

Research questions

The focus of our research was to analyze what type of games were used in computer science, the subject's topics that were covered by the game and pedagogical strategies for implementing game-based learning, comparing all these in different educational levels. Starting from this, our research questions are:

RQ1: What kind of educational games are usually used during the implementation of the game-based approach in computer science?

Various games are used to cover topics from computer science, from block-based serious games (Vahldick et al., 2020 ) to educational escape rooms (López-Pernas et al., 2019 ). Using different games influences the learning process differently (Chang et al., 2020 ). The RQ1 seeks to identify and understand the types of educational games that are commonly utilized in the context of teaching computer science. Exploration of the variety of used games provides insights into the different approaches, mechanics, and formats used to enhance learning outcomes.

RQ2: Which pedagogical strategy is mostly used in the published research?

There are various strategies for implementing game-based learning in computer science education. The implementation strategies refer to whether students should learn by playing the game (Malliarakis et al., 2014 ) or by designing a game (Denner et al., 2012 ). The strategies can differ based on the gender of students (Harteveld et al., 2014 ), students' age (Bers, 2019 ), or the adopted approach by policymakers (Lindberg et al., 2019 ). RQ2 aims to identify the predominant pedagogical strategy employed in the published research on game-based approaches in computer science education. By examining the pedagogical strategies, researchers can gain insights into the most effective instructional methods that facilitate learning through game-based approaches. Furthermore, the findings can inform educators and researchers in designing and implementing effective instructional strategies that align with the goals of computer science education.

RQ3: Which computer science topics are covered by the game-based approach?

Game-based learning can be used to teach different computer science topics, from introduction topics (Fagerlund et al., 2021 ; Mathew et al., 2019 ), to core topics (Karram, 2021 ). RQ3 aims to provide value in exploring the specific computer science topics addressed through game-based approaches. In addition, it helps identify the range of topics that have been integrated into educational games. By understanding the computer science topics covered, researchers can assess the breadth and depth of the game-based approach and identify potential gaps or areas for further exploration in the curriculum.

RQ4: What are the potential research topics concerning the implementation of a game-based approach in computer science?

RQ4 is essential as it seeks to identify potential areas for future research in the implementation of game-based approaches in computer science education. It might include specific computer science topics (Calvo-Morata et al., 2020 ), strategies to implement game-based learning in computer science (Hooshyar et al., 2021 ), or ways to analyze the effects of game-based learning (Scherer et al., 2020 ). By exploring research topics that have not been extensively studied or require further investigation, researchers can identify new directions and opportunities for advancing the field. This can contribute to the ongoing development and improvement of game-based approaches in computer science education, fostering innovation and addressing emerging challenges.

Methodology

To answer research questions, we analyzed the contents of articles published from 2017 to 2021. Due to the rapid development of technology and change in the learnt computer science topics as well as designed game with new technology and tools, we have decided to research the articles that refer just to the interval of 5 years. As technology progresses swiftly, studying 5 year interval of the published literature ensures that scoping review results analyze the most current tools, approaches, and methodologies being utilized in the field of computer science education.

The research was done according to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines (Peters et al., 2020 ). The PRISMA-ScR methodology is a structured approach used to conduct comprehensive and transparent scoping reviews. It involves identifying a research question, performing a systematic search of relevant literature, applying inclusion and exclusion criteria to select studies, extracting data from the included studies, analyzing and synthesizing the data to identify key themes or patterns, and reporting the findings. It aims to map the existing literature on a particular topic, identify key concepts, and examine the extent, range, and nature of research available. It is particularly useful for exploring complex and diverse research questions.

There is a large number of articles regarding the topic, so performing this kind of research manually seemed like labor-intensive work. Therefore, we have identified the opportunity to use the Natural Language Processing (NLP) toolkit (Zdravevski et al., 2019 ) to automate the literature search, scanning, and eligibility assessment. We have used this toolkit for article identification and selection (i.e., scanning procedures and eligibility criteria assessment). The search considered articles indexed in four digital libraries: IEEE, PubMed, Springer and Elsevier. The NLP toolkit requires structured data input comprising keywords, properties, property groups, required relevance, included sources, and start and end years.

The provided keywords serve as search criteria within available libraries, acting as the primary filter to determine which articles will be gathered for further analysis. At the beginning of setting up the NLP toolkit for the research, to address different games that can be used in education, we have identified the main keywords to be "Serious Games", "Educational Games", "Games in education" or "Games for learning". The NLP toolkit used these keywords to identify the potentially relevant articles in the mentioned digital libraries.

Furthermore, the NLP toolkit was adjusted to search specific properties (words or phrases) within the title, abstract, or keywords of already identified articles to select relevant articles in more detail, according to the features (properties groups) of the game-based learning approach that we are interested in: subject, educational level, educational context, purpose and used technology. Properties groups address synonyms and various versions of the phrase (e.g., educational games and serious games). To be included in the results, at least one representative from each property group must appear in the title or abstract of the article, thereby functioning as a secondary filter for identifying relevant articles.

The property group "subject" was set as mandatory during the search, because we were interested in analyzing articles that refer to game-based learning just in computer science. Since the name of this subject is different in different countries, we have used synonyms, such as "programming", "coding", and "informatics". The property group "age" or educational level included different synonyms for primary and secondary education, as well as higher education, although we did not make this property mandatory. To search about the used technology (web, online, mobile, augmented reality, virtual reality), we have set one property group to include a different kind of used technology, and we also set a property group that refers to the aim of using these educational games (to achieve students' engagement, increase motivation, evaluation of educational results, etc.). A more detailed description of the properties groups is given in Table 2 .

The following input parameter for the NLP toolkit set-up is the minimum relevant properties. In this research, it was set that each article has to contain a minimum of two of the previously defined properties to be considered relevant. The quality analysis of the relevant articles followed in the next step of the methodology.

Study selection

The initial search in four digital libraries: IEEE, PubMed, Springer and Elsevier, has identified 43,885 articles concerning using game-based learning in computer science. After articles had been identified based on the specified keywords and retrieved from the publishers, the duplicates were identified according to the article DOI as their unique identifier and removed, which has decreased the number of articles to 21,002. In the next step, the articles selection (screening and eligibility assessment) procedures followed, discarding articles not published in the required period or for which the title or abstract could not be analyzed because of parsing errors, unavailability, or other reasons. The screening process eliminated 11,129 articles and the remaining 9873 articles underwent an automated eligibility assessment using the advanced NLP toolkit functionalities. The automated eligibility analysis involved the following processing: tokenization of sentences (Manning et al., 2014 ; Webster et al., 1992 ) and English stop words removal, stemming, and lemmatization using the Natural Language Toolkit library (Bird, 2006 ). Furthermore, articles containing less than two properties were removed, which left 1209 articles eligible for further manual analysis and inclusion in identifying the research trends and summarizing the results.

For each of the articles from the collection of relevant articles, the toolkit automatically generated a bibliographic file (as defined by BibTeX reference management software). This file was manually analyzed in more detail to identify the most relevant articles for the purpose of our study. First, the abstract was read to see whether the article was relevant, and if that did not provide enough information, the whole article was read. For each of the research questions we used the same approach, but with different focuses. For the first research question, we looked for any specific game name. For the second research question, we were looking for any mentioning of the pedagogical approaches or strategies. For the third research question, we looked for different computer science topics used in computer science curricula. In that way, the most relevant articles concerning first three research questions were identified. The last research question is related to future potential research topics in the field of game-based learning in computer science education, so it was not used during this phase of selection of relevant articles.

As a result of the manual analysis of articles’ titles, articles that did not refer to computer science subjects were excluded, which left just 206 articles. We could not obtain the full text for some of articles, so they were excluded from further analyses. Some articles did not refer to using games to teach computer science topics, so they were also removed. The same was the case with a few articles not written in English. Finally, we had 125 relevant articles.

Nine relevant articles were review papers that referred to different game-based learning approaches at different educational levels. Among identified articles is a book describing different teaching methods in computer science education, including game-based learning (Hazzan et al., 2020 ). Two book chapters refer to different approaches of using game-based learning in education (Bellas et al., 2018 ; Zaw & Hlaing, 2020 ). These articles were also excluded from the list.

Finally, we finished the selection process and got 113 relevant articles using educational games in computer science that were the subject of further analysis.

The information flowchart presenting the numbers of identified, screened, processed, and removed articles in the automated NLP procedure and articles removed during the manual analysis is presented in Fig.  1 .

Flowchart of the PRISMA-SCR-based selection process

After the final identification of the most relevant studies concerning game-based learning in computer science, summaries were developed for each article. Information about their correspondence to education, educational level, used game, type of the game, covered computer science topic, educational context and general usefulness of the article was provided.

Distribution of published articles through the years

The distribution of the articles concerning the game-based approach in computer science through the years is presented in Fig.  2 . It can be noticed that the number of articles was increasing through the years, but then suddenly, in 2021, that number decreased. The reason might be found in the situation with the pandemic, because in 2020 and 2021, most of the schools were closed. In some of them, the teaching was transferred online, which resulted in a huge change in the way of teaching and learning, and it was a period of adaptation for teachers and students at the same time, which might lead to a decrease of the research articles.

Distribution of the published articles through the years

Distribution of published articles per country

The distribution of the published articles per country differs from country to country. Figure  3 presents the distribution of published articles per country, showing only the countries that have more than five published articles concerning game-based learning between 2017 and 2021. Most articles are published in the United States, followed by Brazil and Greece.

Distribution of the published articles per country, showing countries with more than five published articles

Further analysis of the relevant articles depending on the country, where the research was conducted, shows that just 17 (of 113) articles are joint work of researchers from different countries. Moreover, just two present joint research on game-based learning from three countries. The first one describes the methodology implemented within the European initiative Coding4girls, which proposes to teach coding through a game design based on a design thinking methodological approach linked to creativity and human-centred solutions (De Carvalho et al., 2020 ). The second joint research (Agbo et al., 2021 ) describes the students’ online co-creation of mini-games to develop their computational thinking skills. Interestingly, all other published articles describe implementing a game-based learning approach in computer science in the local context, making it difficult to generalize the conclusions and the research outcomes.

Distribution of published articles by publisher

Most of the relevant researched articles are published by IEEE Xplore (86 of 113) but mostly published as part of the proceedings at different conferences. This might explain why the number of published articles from IEEE Xplore differs from other publishing companies. Figure  4 presents the distribution of the articles by each of the publishers in detail, comparing published articles in journals and at conferences.

Distribution of the published articles by different publishers

Distribution of published articles by educational level

Identifying the number of articles according to the educational level was more complicated due to the different educational systems in different countries, resulting in a different understanding of the terms “primary”, and “secondary” education. In some countries, the same educational level is entitled as “primary”, and in others as “lower secondary” or even “middle school”. For example, in some countries, the primary school includes 6–14-year-old students; in others, it is divided, so there are primary (from 6 to 10 years), middle (11–13 years) and high schools (14–18 years); and in some, there are even lower secondary school (12–16 years). Therefore, we have tried to combine different categories according to the student’s age and to gather three levels: primary, secondary and university, according to the local context (primary education includes 6–14 years, secondary education includes 15–18 years). The situation with the distribution of the relevant articles is presented in Fig.  5 .

Distribution of the published articles in different educational levels

It can be noticed that most of the articles concern universities, although the number of articles that concern using games in computer science in primary and secondary schools is not small. It can be expected, because most of the articles refer to using games for developing programming skills, which is present mainly at the university level. However, in some countries, primary school students learn fundamental programming concepts.

Distribution of published articles by the purpose of implementation

The purpose of the research concerning game-based learning in computer science is different and mostly depends on the type of the game as well as the topic that is covered by the game. The distribution of the published articles according to the purpose of the implementation of the research is presented in Fig.  6 . However, it must be mentioned that it was difficult to distinguish the purposes of implementing the game-based approach in computer science, because the purpose was not clearly stated in the articles or there was overlapping among different categories.

Distribution of the published articles according to the purpose of the implementation

In the most articles (66 of 113), the research is done to measure students’ learning achievement or to evaluate the benefits of the game-based approach by comparing students’ knowledge and skills before and after implementing this approach. In addition, some articles are interested in students’ engagement and raising students’ interest and motivation for the learning process by implementing a game-based approach. However, just a few articles refer to using this approach for measuring students’ overall satisfaction with the whole experience (3 of 113).

Distribution of published articles by implemented pedagogical strategy and used technology

Manual analyses of the included articles gave us insight into additional aspects of implementing a game-based approach in computer science. When we talk about the game-based approach, there are two main pedagogical strategies for implementation: students can learn by playing the game, and students can learn while creating the game. The distribution of those two approaches in the published articles indicates that learning by playing games is more frequently used than learning by creating games. Only 19 of 113 relevant articles refer to the implementation of a game-based approach, where students learn during the process of game design or are involved themselves in the creation of the game. In most of the articles, students just use the created game (previously created or designed for the purpose of the research) to develop their competencies on a given topic. Regarding the technology used for the creation of the games in the published articles, it can be noticed that most of the games are web-based (although they have a mobile version, too), and there are just a few articles concerning the use of the unplugged activities as a game-based approach for learning computer science.

Distribution of published articles by covered computer science topic

Most of the articles concerning computer science topics covered during the implementation of the game-based approach refer to using to develop students’ programming skills in object-oriented programming, followed by the articles concerning block-based programming and the development of computational thinking skills. The number of articles that utilize the game-based approach in all other computer science topics is significantly smaller (in total, 14 from 113 articles). Figure  7 contains more detailed information about this distribution.

Distribution of the published articles according to the covered computer science topics

Types of educational games used for implementation of the game-based approach in computer science

Our research aims to provide information about the latest research trends concerning game-based learning in computer science education. Table 3 gives information about the implemented game, the type of the game, the computer science topic covered by the game, and the educational level, where the research concerning the game-based approach in computer science was carried out. The type of the game refers to the origin of the game creation, whether the game was already created and can be used or is created for the research by the author or by the students (they are learning during the game design process).

Detailed analysis of these relevant articles shows that different educational games are used to implement game-based learning in computer science, implementing different technologies for their design. Articles refer to using different platforms, environments or engines for creating games using different technology. In primary education, most implemented approaches include block-based environments, such as Blocky, Snap!, and Scratch. Those platforms give access to the already created game (De Carvallho et al., 2020 ; Sáiz Manzanares et al., 2020 ; Vourletsis & Politis, 2022 ) but also offer possibilities a game to be created by a teacher (Bevčič & Rugelj, 2020 ; Holenko Dlab & Hoic-Bozic, 2021 ; Wong & Jiang, 2018 ) or by the students during the learning process (Funke et al., 2017 ; Zeevaarders & Aivaloglouor, 2021 ). Even more, their use as a platform to code Arduino boards is presented in two of the articles (Sharma et al., 2019 ; Yongqiang et al., 2018 ). Block-based environments are used in the research in secondary education, too. For example, Araujo et al. ( 2018 ) measured students’ motivation for learning block-based programming by involving students in creating games in Scratch. Schatten and Schatten ( 2020 ) involve students in creating different games using CodeCombat during the CodeWeek initiative to increase their interest in programming, and Chang and Tsai ( 2018 ) are implementing an approach for learning programming in pairs while coding Kinnect with Scratch.

However, in the research articles concerning secondary education, it can be noticed that some specified games are created by the researcher (or teacher) to develop some concrete computer science skills. In these cases, the articles focus on the evaluation of the effectiveness of the game as an approach. For example, the chatbot’s serious game “PrivaCity” (Berger et al., 2019 ) is designed to raise students’ privacy awareness, as a very important topic among teenagers.

Similarly, “Capture the flag” is a game designed for learning about network security in a vocational school (Prabawa et al., 2017 ). The effectiveness of using the educational game “Degraf” in a vocational high school as supplementary material for learning graphic design subjects is measured by Elmunsyah et al. ( 2021 ). Furthermore, Hananto and Panjaburee ( 2019 ) developed the semi-puzzle game “Key and Chest” to develop algorithm thinking skills and concluded that this digital game could lead to better achievement than if the physical game is used for the same purpose. The number of games developed at the university level on a specific topic by the researchers is even more significant. However, there is still no standardized game, and the games differ among themselves depending on the topic covered by the game and the country, where the game is implemented.

Only a few games are mentioned more than once in the list of relevant articles. The implementation of “Code defenders” to enable students to learn about software testing in a fun and competitive way is researched by Clegg et al. ( 2017 ) and Fraser et al. ( 2020 ). However, the studies continue each other, presenting improvements in the game. Different block-based programming languages and online platforms such as Scratch, Snap!, and Code Combat are mentioned in several articles, too. Implementation of a game-based approach during the assessment process through the creation of quizzes in Kahoot is presented by Abidin and Zaman ( 2017 ) and Videnovik et al. ( 2018 ). Finally, several articles refer to the use of Escape room as a popular game implemented in an educational context (Giang et al., 2020 ; López-Pernas et al., 2019 , 2021 ; Seebauer et al., 2020 ; Towler et al., 2020 ). However, all these Escape room-style games are created on different platforms and cover different topics. Therefore, it can be concluded that no standardized type of game is implemented at a certain educational level or concerning a specific topic.

Further analyses were done concerning the type of the game, referring to the origin of the game: already created and just used for the research, created by the researcher for the purpose of the research or created by the students during the learning process. The distribution of the number of articles according to the type of the game in different educational levels is presented in Fig.  8 .

Distribution of the published articles according to the game designer in different educational levels

Most of the articles describe the implementation of a game-based approach when the author creates the game to test the game’s efficiency and make improvements based on the feedback received by the students. The number of games created by the author is the biggest at the university level, and the most balanced distribution of different kinds of games (created by the author, students or already created) is present in primary education. Interestingly, the most significant number of articles that concern using games created by students is in primary education. It shows that students in primary education have been the most involved in the process of game design, although they are young and have less knowledge and skills than students at other educational levels. This could be result of the fact that the articles that refer to primary education present a game’s design only in a block-based environment and using basic programming concepts. However, research articles do not refer to a standardized methodology of a framework for the creation of a game, and each game is designed individually depending on the used technology, topic and educational level.

Pedagogical strategies for implementation of the game-based approach in computer science

A detailed analysis of the pedagogical strategies for implementing a game-based approach shows that most relevant articles use games as a tool for learning the content. This trend continues in the recent period as well (Kaldarova et al., 2023 ). Hence, students play the game (already created or created by an author) to gather knowledge or develop their skills. Detail distribution of the research articles regarding pedagogical strategies for implementing a game-based approach is presented in Fig.  9 and more detailed data can be found in Table 3 . Some articles explain how students learn during the process of the creation of a game. Those are different games at different educational levels, but they all concern the process of designing a game on some platform that will develop their programming skills. Unfortunately, no article describes the process of developing students’ knowledge and skills on different computer science topics than programming while designing a game. It is a critical gap that should be considered as a topic in future research: to see whether students can learn about other computer science topics during the game creation process (while they develop their programming skills).

Distribution of the published articles according to the implemented pedagogical strategy

Computer science topics covered by game-based approach in computer science

Figure  10 gives insight into the distribution of the relevant articles concerning the computer science topic covered by the game-based approach. The topic that is mainly taught by a game-based approach at university is object-oriented programming. The situation is similar in secondary schools. Game-based approach is suitable classroom strategy for fostering higher order thinking skills, such as problem solving, group collaboration, and critical thinking, that are developed during learning object-oriented programming, which is consistent with previous research conducted by Chen et al. ( 2021 ).

Distribution of the published articles concerning the covered computer science topics

This can be expected, because the topic is complex for the students, and teachers must find different approaches and strategies to make it more understandable. In addition, in those educational levels, there is a distribution of the articles in different mentioned computer science topics (although it is not equally distributed).

However, if we analyze the topics covered by the game-based approach in primary education, it can be noticed that this approach is implemented in several topics only, mainly connected with the development of students’ computational thinking skills and fundaments of programming languages (see Table 3 for detailed overview). This trend continues in the recent years (Cheng et al., 2023 ; Mozelius & Humble, 2023 ).

Students in primary education mostly learn block-based programming languages, so it is expected that this will be the most frequent topic covered by the game-based approach. However, some articles also refer to object-oriented programming taught in upper grades. The interesting finding is that there are no articles about using educational games to learn other computer science topics, such as hardware, some applications, networks, and cybersecurity, in primary education, as there are in other educational levels. For example, there are two articles that elaborate on learning about internet safety using games in secondary education (Berger et al., 2019 ; Prabawa et al., 2017 ), and no article on game-based learning for internet safety in primary education. This lack of research articles concerning using the game-based approach for learning other topics in computer science in primary education can help identify potential future research topics.

Potential research topics concerning the game-based approach in computer science

While the lack of research articles concerning using the game-based approach for learning other topics in computer science in primary education is a good starting point for identifying potential future research topics, it is important to consider it in combination with practical constraints such are lack of knowledge, access to technology or teacher training on a specific subject. In that context, “Identifying the challenges, opportunities and solutions for integrating game-based learning methods in primary schools for specific computer science topics” can be a future research topic. It should be noted, that although some articles on specific topics can be found in the recent literature (Alam, 2022 ), there is a huge pool of topics, such are internet safety and digital citizenship that can be explored in this context.

There is an evident lack of articles on the use of game-based learning in primary and secondary schools. The findings in the existing literature that elaborate on how specific game design elements influence the learning process are minimal (Baek & Oh, 2019 ; Dos Santos et al., 2019 ; Emembolu et al., 2019 ; Kanellopoulou et al., 2021 ). These findings, combined with the finding of a limited number of articles that use existing games in the process of learning, define the potential future research topic "Assessing the role of game design elements in enhancing engagement and understanding of computer science concepts among primary and/or secondary school students". This research topic can use conceptual framework that investigates how specific elements of game design can contribute to increased engagement and improved understanding of computer science concepts in primary or/and education.

This research topic includes various specific research questions and theoretical frameworks. One possible set of research questions can investigate the specific elements of game design that can be incorporated into educational games or learning activities to enhance the learning experience. These elements may include interactive interfaces, engaging narratives, immersive environments, feedback mechanisms, competition or collaboration features, levels of difficulty, rewards, and progression systems. Different theories such are social cognitive theory (Lim et al., 2020 ) and self-determination theory (Ryan et al., 2006 ) can be used to better understand the motivational factors of different game design elements (interactivity, challenges, and rewards), and how they influence student engagement and sustain student interest and active participation in computer science learning.

All mentioned research questions can be investigated by conducting experiments, surveys, observations, or interviews to gather quantitative and qualitative data on student experiences and perceptions. Combined with data from learning outcomes, these potential findings can provide the information about overall effectiveness of using the elements of a game-based approach to learning computer science in primary schools.

Limitations

This scoping review focuses on the articles in four digital libraries, potentially leaving a significant number of articles out of the analyzing process.

Using the NLP toolkit automates searching for relevant articles. Undoubtedly, a human reader might better understand the context and better assess the relevance of an article and potentially include some articles that NLP toolkit classified as irrelevant. In addition, after the initial selection by NLP toolkit, we performed the quality assessment of the identified articles, for each of the research questions. In that way, we ensured that only relevant articles are included in the study, but it might happen that, due to the phase of selection some relevant articles were omitted from the study.

Detailed meta-analyses within the selected group of articles concerning a particular research feature can further contribute to the existing body of knowledge. Similar analyses exist, but not on learning computer science (Gui et al., 2023 ). For example, in our manuscript, we did not consider the size of the student population, existence of the control group of students, or replicability of the studies.

This scoping review discusses implementation of game-based approach in computer science by analyzing research articles in four digital libraries published between 2017 and 2021. In total, 113 research articles were analyzed concerning the educational level, where the game-based approach is implemented, the type of the game, covered computer science topic, pedagogical strategy and purpose of the implementation. The results show that the number of research articles is increasing through the years, confirming the importance of implementing a game-based approach in computer science. Most of these articles refer to the research in just one country, in the local context, making it difficult to generalize the research outcomes and conclusions on the international level.

The article presents various games using various technologies concerning several computer science topics. However, there is no standardized game or methodology that can be used for designing an educational game. Implemented game in each of the researched articles depends on the educational level, covered topic and game type. From our findings, it is evident that most articles refer to the implementation of the game-based approach, where students gather the necessary knowledge and skills while playing a game. Just a few of them incorporate the process of learning by designing educational games, and this learning is connected to developing computational thinking or programming skills.

Potential future research might be focused on identifying the challenges, opportunities, and solutions for integrating game-based learning methods for a specific computer science topic. Example topics might be internet safety and digital citizenship.

The lack of research articles on game-based learning in primary and secondary schools, along with limited findings on the influence of game design elements, highlights the need to assess how different elements enhance engagement and understanding of computer science concepts.

Availability of data and materials

All data generated and analyzed during this study are included in this article.

https://scratch.mit.edu/

https://snap.berkeley.edu/

https://blockly.games/

https://codecombat.com/

https://www.alice.org/

https://greenfoot.org/door

Abbasi, S., Kazi, H., & Khowaja, K. (2017). A systematic review of learning object-oriented programming through serious games and programming approaches. In 2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICETAS.2017.8277894

Abdellatif, A. J., McCollum, B., & McMullan, P. (2018). Serious games quality characteristics evaluation: The case study of optimizing Robocode. In 2018 International Symposium on Computers in Education (SIIE) (pp. 1–4). IEEE. https://doi.org/10.1109/SIIE.2018.8586730

Abidin, H. Z., & Kamaru Zaman, F. H. (2017). Students’ perceptions on game-based classroom response system in a computer programming course. In 2017 IEEE 9th International Conference on Engineering Education (ICEED) (pp. 254–259). IEEE. https://doi.org/10.1109/ICEED.2017.8251203

Agalbato, F., & Loiacono, D. (2018). Robo 3 : A puzzle game to learn coding. In 2018 IEEE Games, Entertainment, Media Conference (GEM) (pp. 359–366). IEEE. https://doi.org/10.1109/GEM.2018.8516515

Agbo, F. J., Oyelere, S. S., Suhonen, J., & Laine, T. H. (2021). Co-design of mini games for learning computational thinking in an online environment. Education and Information Technologies, 26 (5), 5815–5849. https://doi.org/10.1007/s10639-021-10515-1

Article   Google Scholar  

Alam, A. (2022). A digital game-based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. In  2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)  (pp. 69–74). IEEE. https://doi.org/10.1109/icscds53736.2022.9760932

Alatrista-Salas, H., & Nunez-Del-Prado, M. (2018). Teaching software engineering through computer games. In 2018 IEEE World Engineering Education Conference (EDUNINE) (pp. 1–4). IEEE. https://doi.org/10.1109/EDUNINE.2018.8450996

Anastasiadis, T., Lampropoulos, G., & Siakas, K. (2018). Digital game-based learning and serious games in education. International Journal of Advances in Scientific Research and Engineering., 4 (12), 139–144. https://doi.org/10.31695/IJASRE.2018.33016

Araujo, L. G. J., Bittencourt, R. A., & Santos, D. M. B. (2018). Contextualized spiral learning of computer programming in Brazilian vocational secondary education. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE.2018.8658456

Arcagök, S. (2021). The impact of game-based teaching practices in different curricula on academic achievement. International Online Journal of Education and Teaching, 8 (2), 778–796.

Google Scholar  

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology , 8 (1), 19–32. https://doi.org/10.1080/1364557032000119616 .

Baek, J., & Oh, G. (2019). Development of a puzzle game to learn coding for elementary students. In C. Stephanidis (Ed.), HCI International 2019 – Late Breaking Papers (pp. 267–279). Springer International Publishing. https://doi.org/10.1007/978-3-030-30033-3_21

Barradas, R., Lencastre, J., Soares, S., & Valente, A. (2020). Developing computational thinking in early ages: A review of the code.org platform. In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU2020) (pp. 157–168). SCITEPRESS – Science and Technology Publications https://doi.org/10.5220/0009576801570168

Barriga, N. A., & Besoain, F. (2020). Artificial intelligence and mobile programming courses for a video game development program in Chile. Computing in Science & Engineering, 22 (4), 17–25. https://doi.org/10.1109/MCSE.2020.2986758

Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2023). The effect of digital game-based learning interventions on cognitive, metacognitive, and affective-motivational learning outcomes in school: A meta-analysis. Review of Educational Research . https://doi.org/10.3102/00346543231167795

Bellas, F., Naya, M., Varela, G., Llamas, L., Prieto, A., Becerra, J. C., Bautista, M., Faiña, A., & Duro, R. (2018). The Robobo project: Bringing educational robotics closer to real-world applications. In W. Lepuschitz, M. Merdan, G. Koppensteiner, R. Balogh, & D. Obdržálek (Eds.), Robotics in education: Latest results and developments (pp. 226–237). Springer. https://doi.org/10.1007/978-3-319-62875-2_20

Chapter   Google Scholar  

Berger, E., Sæthre, T. H., & Divitini, M. (2019). PrivaCity: A chatbot game to raise privacy awareness among teenagers. In S. N. Pozdniakov & V. Dagienė (Eds.), Informatics in schools. New ideas in school informatics (pp. 293–304). Springer. https://doi.org/10.1007/978-3-030-33759-9_23

Bers, M. U. (2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal of Computers in Education, 6 (4), 499–528. https://doi.org/10.1007/s40692-019-00147-3

Bevcic, M., & Rugelj, J. (2020). Game design-based learning of programming for girls. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 576–580). IEEE. https://doi.org/10.23919/MIPRO48935.2020.9245175

Bird, S. (2006). NLTK: the natural language toolkit. In  Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions  (pp. 69–72). https://doi.org/10.3115/1225403.1225421

Borna, K., & Rad, H. M. (2018). Serious games in computer science learning goals. In 2018 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC) (pp. 161–166). IEEE. https://doi.org/10.1109/DGRC.2018.8712030

Boyer, S. L., Edmondson, D. R., Artis, A. B., & Fleming, D. (2014). Self-directed learning: A tool for lifelong learning. Journal of Marketing Education, 36 (1), 20–32. https://doi.org/10.1177/0273475313494010

Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., & Pereira, J. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94 , 178–192. https://doi.org/10.1016/j.compedu.2015.11.003

Calvo-Morata, A., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2020). Serious games to prevent and detect bullying and cyberbullying: A systematic serious games and literature review. Computers & Education, 157 , 103958. https://doi.org/10.1016/j.compedu.2020.103958

Campbell, L. (2020). Teaching in an inspiring learning space: An investigation of the extent to which one school’s innovative learning environment has impacted on teachers’ pedagogy and practice. Research Papers in Education, 35 (2), 185–204. https://doi.org/10.1080/02671522.2019.1568526

Cardinot, A., & Fairfield, J. A. (2019). Game-based learning to engage students with physics and astronomy using a board game. International Journal of Game-Based Learning, 9 (1), 42–57. https://doi.org/10.4018/IJGBL.2019010104

Chandel, P., Dutta, D., Tekta, P., Dutta, K., & Gupta, V. (2015). Digital game-based learning in computer science education. CPUH-Research Journal, 1 (2), 33–37.

Chang, C. S., Chung, C. H., & Chang, J. A. (2020). Influence of problem-based learning games on effective computer programming learning in higher education. Educational Technology Research and Development, 68 , 2615–2634. https://doi.org/10.1007/s11423-020-09784-3

Chang, C. K., & Tsai, Y. T. (2018). Pair-programming curriculum development of motion-based game for enhancing computational thinking skills. In  2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)  (pp. 284–287). IEEE. https://doi.org/10.1109/IIAI-AAI.2018.00061

Chen, P. Y., Hwang, G. J., Yeh, S. Y., Chen, Y. T., Chen, T. W., & Chien, C. H. (2021). Three decades of game-based learning in science and mathematics education: An integrated bibliometric analysis and systematic review. Journal of Computers in Education, 9 (3), 455–476. https://doi.org/10.1007/s40692-021-00210-y

Chen, P., Yang, D., Metwally, A. H. S., Lavonen, J., & Wang, X. (2023). Fostering computational thinking through unplugged activities: A systematic literature review and meta-analysis. International Journal of STEM Education, 10 (1), 1–25. https://doi.org/10.1186/s40594-023-00434-7

Cheng, Y. P., Lai, C. F., Chen, Y. T., Wang, W. S., Huang, Y. M., & Wu, T. T. (2023). Enhancing student’s computational thinking skills with student-generated questions strategy in a game-based learning platform. Computers & Education, 200 , 104794. https://doi.org/10.1016/j.compedu.2023.104794

Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86 , 79–122. https://doi.org/10.3102/0034654315582065

Clegg, B. S., Rojas, J. M., & Fraser, G. (2017). Teaching software testing concepts using a mutation testing game. In 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering Education and Training Track (ICSE-SEET) (pp. 33–36). IEEE. https://doi.org/10.1109/ICSE-SEET.2017.1

Corda, F., Onnis, M., Pes, M., Spano, L. D., & Scateni, R. (2019). BashDungeon: Learning UNIX with a video-game. Multimedia Tools and Applications, 78 (10), 13731–13746. https://doi.org/10.1007/s11042-019-7230-3

Daungcharone, K., Panjaburee, P., & Thongkoo, K. (2017). Using digital game as compiler to motivate C programming language learning in higher education. In 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 533–538). IEEE. https://doi.org/10.1109/IIAI-AAI.2017.77

De Carvalho, C. V., Cerar, Š, Rugelj, J., Tsalapatas, H., & Heidmann, O. (2020). Addressing the gender gap in computer programming through the design and development of serious games. IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje, 15 (3), 242–251. https://doi.org/10.1109/RITA.2020.3008127

De Kereki, I. F., & Adorjan, A. (2018). Serious games: Using abstract strategy games in computer science 2: An experience report and lessons learned. In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 169–174). IEEE. https://doi.org/10.1109/EDUCON.2018.8363224

De Troyer, O., Lindberg, R., Maushagen, J., & Sajjadi, P. (2019). Development and evaluation of an educational game to practice the truth tables of logic. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) (pp. 92–96). IEEE. https://doi.org/10.1109/ICALT.2019.00032

Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58 (1), 240–249. https://doi.org/10.1016/j.compedu.2011.08.006

Díaz, J., López, J. A., Sepúlveda, S., Ramírez Villegas, G. M., Ahumada, D., & Moreira, F. (2021). Evaluating aspects of usability in video game-based programming learning platforms. Procedia Computer Science, 181 , 247–254. https://doi.org/10.1016/j.procs.2021.01.141

Dočkalová Burská, K., Rusňák, V., & Ošlejšek, R. (2022). Data-driven insight into the puzzle-based cybersecurity training. Computers & Graphics, 102 , 441–451. https://doi.org/10.1016/j.cag.2021.09.011

Dos Santos, A. L., Souza, M. R. D. A., Dayrell, M., & Figueiredo, E. (2018). Exploring game elements in learning programming: An empirical evaluation. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE.2018.8658505

Dos Santos, A. L., Souza, M. R. D. A., Dayrell, M., & Figueiredo, E. (2019). A systematic mapping study on game elements and serious games for learning programming. In B. M. McLaren, R. Reilly, S. Zvacek, & J. Uhomoibhi (Eds.), Computer supported education (Vol. 1022, pp. 328–356). Springer. https://doi.org/10.1007/978-3-030-21151-6_17

Duch, P., & Jaworski, T. (2018). Enriching computer science programming classes with Arduino game development. In 2018 11th International Conference on Human System Interaction (HIS) (pp. 148–154). IEEE. https://doi.org/10.1109/HSI.2018.8430994

Eleftheriadis, S., & Xinogalos, S. (2020). Office Madness: Design and pilot evaluation of a serious game for learning the C++ programming language. In I. Marfisi-Schottman, F. Bellotti, L. Hamon, & R. Klemke (Eds.), Games and learning alliance (pp. 389–394). Springer. https://doi.org/10.1007/978-3-030-63464-3_36

Elmunsyah, H., Kusumo, G. R., Pujianto, U., & Prasetya, D. D. (2018). Development of mobile based educational game as a learning media for basic programming in VHS. In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 416–420). IEEE. https://doi.org/10.1109/EECSI.2018.8752658

Elmunsyah, H., Herwanto, H. W., Smaragdina, A. A., Anggraini, N. S., & Utomo, W. M. (2021). DEGRAF educational game as a supplement for basic graphic design subjects for vocational high school students. In 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 128–132). IEEE. https://doi.org/10.1109/ICEEIE52663.2021.9616829

Emembolu, I., Strachan, R., Davenport, C., Dele-Ajayi, O., & Shimwell, J. (2019). Encouraging diversity in computer science among young people: Using a games design intervention based on an integrated pedagogical framework. In 2019 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE43999.2019.9028436

Evripidou, S., Amanatiadis, A., Christodoulou, K., & Chatzichristofis, S. A. (2021). Introducing algorithmic thinking and sequencing using tangible robots. IEEE Transactions on Learning Technologies, 14 (1), 93–105. https://doi.org/10.1109/TLT.2021.3058060

Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational thinking in programming with Scratch in primary schools: A systematic review. Computer Applications in Engineering Education, 29 (1), 12–28. https://doi.org/10.1002/cae.22255

Fraser, G., Gambi, A., & Rojas, J. M. (2020). Teaching software testing with the Code Defenders testing game: Experiences and improvements. In 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 461–464). IEEE. https://doi.org/10.1109/ICSTW50294.2020.00082

Funke, A., Geldreich, K., & Hubwieser, P. (2017). Analysis of Scratch projects of an introductory programming course for primary school students. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 1229–1236). IEEE. https://doi.org/10.1109/EDUCON.2017.7943005

Gabaruk, J., Logofatu, D., Groskreutz, D., & Andersson, C. (2019). On teaching Java and object oriented programming by using children board games. In 2019 IEEE Global Engineering Education Conference (EDUCON) (pp. 601–606). IEEE. https://doi.org/10.1109/EDUCON.2019.8725264

Garcia-Ruiz, M. A., Alvarez-Cardenas, O., & Iniguez-Carrillo, A. L. (2021). Experiences in developing and testing BBC Micro: Bit games in a K-12 Coding Club during the COVID-19 Pandemic. In 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall) (pp. 161–164). IEEE. https://doi.org/10.1109/ICISFall51598.2021.9627364

Gardeli, A., & Vosinakis, S. (2019). ARQuest: A tangible augmented reality approach to developing computational thinking skills. In 2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games) (pp. 1–8). IEEE. https://doi.org/10.1109/VS-Games.2019.8864603

Ghergulescu, I., & Muntean, C. H., et al. (2012). Measurement and analysis of learner’s motivation in game-based e-learning. In D. Ifenthaler (Ed.), Assessment in game-based learning: Foundations, innovations, and perspectives (pp. 355–378). Cham: Springer. https://doi.org/10.1007/978-1-4614-3546-4_18

Giang, C., Chevalier, M., Negrini, L., Peleg, R., Bonnet, E., Piatti, A., & Mondada, F. (2020). Exploring Escape games as a teaching tool in educational robotics. In M. Moro, D. Alimisis, & L. Iocchi (Eds.), Educational robotics in the context of the maker movement (pp. 95–106). Springer. https://doi.org/10.1007/978-3-030-18141-3_8

Gossen, F., Kuhn, D., Margaria, T., & Lamprecht, A. (2018). Computational thinking: Learning by doing with the Cinco adventure game tool. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (pp. 990–999). IEEE. https://doi.org/10.1109/COMPSAC.2018.00175

Groza, A., Baltatescu, M. M., & Pomarlan, M. (2020). MineFOL: A game for learning first order logic. In 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 153–160). IEEE. https://doi.org/10.1109/ICCP51029.2020.9266174

Gui, Y., Cai, Z., Yang, Y., Kong, L., Fan, X., & Tai, R. H. (2023). Effectiveness of digital educational game and game design in STEM learning: A meta-analytic review. International Journal of STEM Education, 10 (1), 1–25. https://doi.org/10.1186/s40594-023-00424-9

Gulec, U., Yilmaz, M., Yalcin, A. D., O’Connor, R. V., & Clarke, P. M. (2019). CENGO: A web-based serious game to increase the programming knowledge levels of computer engineering students. In A. Walker, R. V. O’Connor, & R. Messnarz (Eds.), Systems, software and services process improvement (pp. 237–248). Springer. https://doi.org/10.1007/978-3-030-28005-5_18

Hafeez, M. (2022). Effects of game-based learning in comparison to traditional learning to provide an effective learning environment: A comparative review. Contemporary Educational Researches Journal, 12 (2), 89–105. https://doi.org/10.18844/cerj.v12i2.6374

Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54 , 170–179. https://doi.org/10.1016/j.chb.2015.07.045

Hananto, A. A. F., & Panjaburee, P. (2019). Proposing an online peer-feedback approach in digital game from a semi-puzzle game-based learning perspective. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 254–259). IEEE. https://doi.org/10.1109/IIAI-AAI.2019.00058

Harteveld, C., Smith, G., Carmichael, G., Gee, E., & Stewart-Gardiner, C. (2014). A design-focused analysis of games teaching computer science. Proceedings of Games+ Learning_ Society, 10 , 1–8.

Hartt, M., Hosseini, H., & Mostafapour, M. (2020). Game on: Exploring the effectiveness of game-based learning. Planning Practice & Research, 35 (5), 589–604. https://doi.org/10.1080/02697459.2020.1778859

Hazzan, O., Ragonis, N., & Lapidot, T. (2020). Teaching methods in computer science education. In O. Hazzan, N. Ragonis, & T. Lapidot (Eds.), Guide to teaching computer science: An activity-based approach (pp. 181–220). Springer. https://doi.org/10.1007/978-3-030-39360-1_10

Hirsh-Pasek, K., Hadani, H. S., Blinkoff, E., & Golinkoff, R. M. (2020). A new path to education reform: playful learning promotes 21st century skills in school and beyond.  Policy Brief . https://www.brookings.edu/wp-content/uploads/2020/10/Big-Ideas_Hirsh-Pasek_PlayfulLearning.pdf

Holanda, M., Araujo, A. P. F., & Walter, M. E. (2020). Meninas.comp project: Programming for girls in high school in Brazil. In 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) (pp. 1–2). IEEE. https://doi.org/10.1109/RESPECT49803.2020.9272419

Holenko Dlab, M., & Hoic-Bozic, N. (2021). Effectiveness of game development-based learning for acquiring programming skills in lower secondary education in Croatia. Education and Information Technologies, 26 (4), 4433–4456. https://doi.org/10.1007/s10639-021-10471-w

Hong, T. Y., & Chu, H. C. (2017). Effects of a situated 3D computational problem-solving and programming game-based learning model on students’ learning perception and cognitive loads. In 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 596–600). IEEE https://doi.org/10.1109/IIAI-AAI.2017.96

Hooshyar, D., Pedaste, M., Yang, Y., Malva, L., Hwang, G.-J., Wang, M., Lim, H., & Delev, D. (2021). From gaming to computational thinking: An adaptive educational computer game-based learning approach. Journal of Educational Computing Research, 59 (3), 383–409. https://doi.org/10.1177/0735633120965919

Horst, R., Naraghi-Taghi-Off, R., Diez, S., Uhmann, T., Müller, A., & Dörner, R. (2019). FunPlogs – a serious puzzle mini-game for learning fundamental programming principles using visual scripting. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, D. Ushizima, S. Chai, S. Sueda, X. Lin, A. Lu, D. Thalmann, C. Wang, & P. Xu (Eds.), Advances in Visual Computing (pp. 494–504). Springer International Publishing. https://doi.org/10.1007/978-3-030-33720-9_38

Huang, L. W., Chen, L. W., & Cheng, P. H. (2019). Board game design for Python programming education. In 2019 IEEE International Conference on Engineering, Technology and Education (TALE) (pp. 1–6). IEEE. https://doi.org/10.1109/TALE48000.2019.9225988

Huang, L. W., Cheng, P. H., & Chen, L. W. (2021). Web-based board game for learning Python. In 2021 IEEE World Conference on Engineering Education (EDUNINE) (pp. 1–6). IEEE. https://doi.org/10.1109/EDUNINE51952.2021.9429144

Huizenga, J., Admiraal, W., Akkerman, S., & Dam, G. T. (2009). Mobile game-based learning in secondary education: Engagement motivation and learning in a mobile city game. Journal of Computer Assisted Learning., 25 (4), 332–344. https://doi.org/10.1111/j.1365-2729.2009.00316.x

Hussein, M. H., Ow, S. H., Cheong, L. S., & Thong, M.-K. (2019). A digital game-based learning method to improve students’ critical thinking skills in elementary science. IEEE Access, 7 , 96309–96318. https://doi.org/10.1109/ACCESS.2019.2929089

Hwang, G. J., Hung, C. M., & Chen, N. S. (2014). Improving learning achievements, motivations and problem-solving skills through a peer assessment-based game development approach. Educational Technology Research and Development, 62 (2), 129–145. https://doi.org/10.1007/s11423-013-9320-7

Jagust, T., Krzic, A. S., Gledec, G., Grgic, M., & Bojic, I. (2018). Exploring different unplugged game-like activities for teaching computational thinking. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). IEEE. https://doi.org/10.1109/FIE.2018.8659077

Joshi, D. R., Adhikari, K. P., Khanal, B., Khadka, J., & Belbase, S. (2022). Behavioral, cognitive, emotional and social engagement in mathematics learning during COVID-19 pandemic. PLoS ONE, 17 (11), e0278052. https://doi.org/10.1371/journal.pone.0278052

Jovanov, M., Ilijoski, B., Stankov, E., & Armenski, G. (2017). Creation of educational games—project based learning in e-learning systems course. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 1274–1281). IEEE. https://doi.org/10.1109/EDUCON.2017.7943012

Kaldarova, B., Omarov, B., Zhaidakbayeva, L., Tursynbayev, A., Beissenova, G., Kurmanbayev, B., & Anarbayev, A. (2023). Applying game-based learning to a primary school class in computer science terminology learning. Frontiers in Education . https://doi.org/10.3389/feduc.2023.1100275

Kanellopoulou, I., Garaizar, P., & Guenaga, M. (2021). First steps towards automatically defining the difficulty of maze-based programming challenges. IEEE Access, 9 , 64211–64223. https://doi.org/10.1109/ACCESS.2021.3075027

Kannappan, V. T., Fernando, O. N. N., Chattopadhyay, A., Tan, X., Hong, J. Y. J., Seah, H. S., & Lye, H. E. (2019). La Petite Fee Cosmo: Learning data structures through game-based learning. In 2019 International Conference on Cyberworlds (CW) (pp. 207–210). IEEE. https://doi.org/10.1109/CW.2019.00041

Kantharaju, P., Alderfer, K., Zhu, J., Char, B., Smith, B., & Ontanon, S. (2020). Modeling player knowledge in a parallel programming educational game. IEEE Transactions on Games, 14 (1), 64–75. https://doi.org/10.1109/TG.2020.3037505

Karagiannis, S., Papaioannou, T., Magkos, E., & Tsohou, A. (2020). Game-based information security/privacy education and awareness: Theory and practice. In M. Themistocleous, M. Papadaki, & M. M. Kamal (Eds.), Information Systems (Vol. 402, pp. 509–525). Cham: Springer. https://doi.org/10.1007/978-3-030-63396-7_34

Karram, O. (2021). The role of computer games in teaching object-oriented programming in high schools—Code Combat as a game approach. WSEAS Transactions on Advances in Engineering Education, 18 , 37–46. https://doi.org/10.37394/232010.2021.18.4

Kazimoglu, C. (2020). Enhancing confidence in using computational thinking skills via playing a serious game: A case study to increase motivation in learning computer programming. IEEE Access, 8 , 221831–221851. https://doi.org/10.1109/ACCESS.2020.3043278

Kintsakis, D., & Rangoussi, M. (2017). An early introduction to STEM education: Teaching computer programming principles to 5 th graders through an e-learning platform: A game-based approach. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 17–23). IEEE. https://doi.org/10.1109/EDUCON.2017.7942816

Klimová, N., Šajben, J., & Lovászová, G. (2021). Online game-based learning through Minecraft: Education edition programming contest. In 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 1660–1668). IEEE. https://doi.org/10.1109/EDUCON46332.2021.9453953

Kucera, E., Haffner, O., & Leskovsky, R. (2020). Multimedia application for object-oriented programming education developed by Unity engine. In 2020 Cybernetics & Informatics (K&I) (pp. 1–8). IEEE. https://doi.org/10.1109/KI48306.2020.9039853

Kurniawati, A., Akbar, N. H., & Prasetyo, D. (2018). Visual learning on mobile phone for introduction basic programming in vocational high school. In 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) (pp. 186–191). IEEE. https://doi.org/10.1109/CENIM.2018.8710873

Kusuma, G. P., Putera Suryapranata, L. K., Wigati, E. K., & Utomo, Y. (2021). Enhancing historical learning using role-playing game on mobile platform. Procedia Computer Science, 179 , 886–893. https://doi.org/10.1016/j.procs.2021.01.078

Lapek, J. (2018). Promoting 21st century skills in problem-based learning environments. CTETE-Research Monograph Series, 1 (1), 66–85. https://doi.org/10.21061/ctete-rms.v1.c.4

Laporte, L., & Zaman, B. (2018). A comparative analysis of programming games, looking through the lens of an instructional design model and a game attributes taxonomy. Entertainment Computing, 25 , 48–61. https://doi.org/10.1016/j.entcom.2017.12.005

Lee, S. M. (2019). Her Story or their own stories? Digital game-based learning, student creativity, and creative writing. ReCALL, 31 (3), 238–254. https://doi.org/10.1017/S0958344019000028

Li, F. Y., Hwang, G. J., Chen, P. Y., & Lin, Y. J. (2021). Effects of a concept mapping-based two-tier test strategy on students’ digital game-based learning performances and behavioral patterns. Computers & Education, 173 , 104293. https://doi.org/10.1016/j.compedu.2021.104293

Lim, J. S., Choe, M. J., Zhang, J., & Noh, G. Y. (2020). The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: A social cognitive theory perspective. Computers in Human Behavior, 108 , 106327. https://doi.org/10.1016/j.chb.2020.106327

Lindberg, R. S., Laine, T. H., & Haaranen, L. (2019). Gamifying programming education in K-12: A review of programming curricula in seven countries and programming games. British Journal of Educational Technology, 50 (4), 1979–1995. https://doi.org/10.1111/bjet.12685

Liu, Z. Y., Shaikh, Z., & Gazizova, F. (2020). Using the concept of game-based learning in education. International Journal of Emerging Technologies in Learning (iJET), 15 (14), 53–64. https://doi.org/10.3991/ijet.v15i14.14675

Lopez-Fernandez, D., Gordillo, A., Alarcon, P. P., & Tovar, E. (2021a). Comparing traditional teaching and game-based learning using teacher-authored games on computer science education. IEEE Transactions on Education, 64 (4), 367–373. https://doi.org/10.1109/TE.2021.3057849

Lopez-Fernandez, D., Gordillo, A., Ortega, F., Yague, A., & Tovar, E. (2021b). LEGO® serious play in software engineering education. IEEE Access, 9 , 103120–103131. https://doi.org/10.1109/ACCESS.2021.3095552

López-Pernas, S., Gordillo, A., Barra, E., & Quemada, J. (2019). Analyzing learning effectiveness and students’ perceptions of an educational Escape room in a programming course in higher education. IEEE Access, 7 , 184221–184234. https://doi.org/10.1109/ACCESS.2019.2960312

López-Pernas, S., Gordillo, A., Barra, E., & Quemada, J. (2021). Comparing face-to-face and remote educational Escape rooms for learning programming. IEEE Access, 9 , 59270–59285. https://doi.org/10.1109/ACCESS.2021.3073601

Lotfi, E., Othman, B. Y., & Mohammed, B. (2019). Towards a mobile serious game for learning object oriented programming paradigms. In M. BenAhmed, A. A. Boudhir, & A. Younes (Eds.), Innovations in smart cities applications Edition 2 (pp. 450–462). Springer. https://doi.org/10.1007/978-3-030-11196-0_38

Lymbery, J. (2012). The potential of a game-based learning approach to improve learner outcomes. Computers in New Zealand Schools, 24 (1), 21–39.

Malizia, A., Fogli, D., Danesi, F., Turchi, T., & Bell, D. (2017). TAPASPlay: A game-based learning approach to foster computation thinking skills. In 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 345–346). IEEE. https://doi.org/10.1109/VLHCC.2017.8103502

Malliarakis, C., Satratzemi, M., & Xinogalos, S. (2014). Educational games for teaching computer programming. Research on e-Learning and ICT in Education Technological, Pedagogical and Instructional Perspectives. https://doi.org/10.1007/978-1-4614-6501-0_7

Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60). https://doi.org/10.3115/v1/P14-5010

Martins, E. R., Geraldes, W. B., Afonseca, U. R., & Gouveia, L. M. B. (2019). Using Kahoot as a learning tool. In I. Ramos, R. Quaresma, P. Silva, & T. Oliveira (Eds.), Information systems for industry 4.0 (Vol. 31, pp. 161–169). Springer. https://doi.org/10.1007/978-3-030-14850-8_11

Mathew, R., Malik, S. I., & Tawafak, R. M. (2019). Teaching problem solving skills using an educational game in a computer programming course. Informatics in Education, 18 (2), 359–373. https://doi.org/10.15388/infedu.2019.17

Meftah, C., Retbi, A., Bennani, S., & Idrissi, M. K. (2019). Evaluation of user experience in the context of mobile serious game. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) (pp. 1–5). IEEE. https://doi.org/10.1109/ISACS48493.2019.9068872

Mezentseva, L. V., Kolganov, S. V., Dvoryankin, O. A., & Tuganova, S. V. (2021). Game-driven learning in the digital age: A systematic review and meta-analysis. Turkish Journal of Computer and Mathematics Education (TURCOMAT)., 12 (10), 2244–2253. https://doi.org/10.17762/turcomat.v12i10.4747

Miljanovic, M. A., & Bradbury, J. S. (2020). GidgetML: An adaptive serious game for enhancing first year programming labs. In 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) (pp. 184–192). IEEE. https://doi.org/10.1145/3377814.3381716

Min, W., Mott, B., Park, K., Taylor, S., Akram, B., Wiebe, E., Boyer, K. E., & Lester, J. (2020). Promoting computer science learning with block-based programming and narrative-centered gameplay. In 2020 IEEE Conference on Games (CoG) (pp. 654–657). IEEE. https://doi.org/10.1109/CoG47356.2020.9231881

Montes, H., Hijon-Neira, R., Perez-Marin, D., & Montes, S. (2021). Using an online serious game to teach basic programming concepts and facilitate gameful experiences for high school students. IEEE Access, 9 , 12567–12578. https://doi.org/10.1109/ACCESS.2021.3049690

Mosquera, C. K., Steinmaurer, A., Eckhardt, C., & Guetl, C. (2020). Immersively learning object oriented programming concepts with sCool. In 2020 6th International Conference of the Immersive Learning Research Network (ILRN) (pp. 124–131). IEEE. https://doi.org/10.23919/iLRN47897.2020.9155144

Mozelius, P., & Humble, N. (2023). Design factors for an educational game where girls and boys play together to learn fundamental programming. In  International Conference on ArtsIT, Interactivity and Game Creation  (pp. 134–148). Springer, Cham. https://doi.org/10.1007/978-3-031-28993-4_10

Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18 (1), 143. https://doi.org/10.1186/s12874-018-0611-x

Nche, O. M., Welter, J., Che, M., Kraemer, E. T., Sitaraman, M., & Zordan, V. B. (2019). CodeTracesure—combining gaming, CS concepts, and pedagogy. In 2019 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) (pp. 1–2). IEEE. https://doi.org/10.1109/RESPECT46404.2019.8985865

Nche, O. M., Boulware, R., Che, S. M., Kraemer, E. T., Sitaraman, M., & Zordan, V. B. (2020). Basic code understanding challenges for elementary school children. In 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) (pp. 1–2). IEEE. https://doi.org/10.1109/RESPECT49803.2020.9272421

Noroozi, O., Dehghanzadeh, H., & Talaee, E. (2020). A systematic review on the impacts of game-based learning on argumentation skills. Entertainment Computing, 35 , 100369. https://doi.org/10.1016/j.entcom.2020.100369

Noval, B. A., Safrodin, M., & Hakkun, R. Y. (2019). Battlebot: Logic learning based on visual programming implementation in multiplayer game online. In 2019 International Electronics Symposium (IES) (pp. 138–142). IEEE. https://doi.org/10.1109/ELECSYM.2019.8901628

Paiva, J. C., Leal, J. P., & Queirós, R. (2020). Authoring game-based programming challenges to improve students’ motivation. In M. E. Auer & T. Tsiatsos (Eds.), The challenges of the digital transformation in education (Vol. 916, pp. 602–613). Springer. https://doi.org/10.1007/978-3-030-11932-4_57

Park, K., Mott, B., Min, W., Wiebe, E., Boyer, K. E., & Lester, J. (2020). Generating game levels to develop computer science competencies in game-based learning environments. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education (Vol. 12164, pp. 240–245). Cham: Springer. https://doi.org/10.1007/978-3-030-52240-7_44

Partovi, T., & Razavi, M. R. (2019). The effect of game-based learning on academic achievement motivation of elementary school students. Learning and Motivation, 68 , 101592. https://doi.org/10.1016/j.lmot.2019.101592

Pellas, N., & Mystakidis, S. (2020). A systematic review of research about game-based learning in virtual worlds. JUCS - Journal of Universal Computer Science, 26 (8), 1017–1042. https://doi.org/10.3897/jucs.2020.054

Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18 (10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167

Pila, S., Aladé, F., Sheehan, K. J., Lauricella, A. R., & Wartella, E. A. (2019). Learning to code via tablet applications: An evaluation of Daisy the Dinosaur and Kodable as learning tools for young children. Computers & Education, 128 , 52–62. https://doi.org/10.1016/j.compedu.2018.09.006

Popovic, J., Korolija, N., Markovic, Z., & Bojic, D. (2017). Developing algorithmic skills of pupils in Serbian schools using code.org materials. In 2017 25th Telecommunication Forum (TELFOR) (pp. 1–4). IEEE. https://doi.org/10.1109/TELFOR.2017.8249483

Prabawa, H. W., Junaeti, E., & Permana, Y. (2017, October). Using capture the flag in classroom: Game-based implementation in network security learning. In  2017 3rd International Conference on Science in Information Technology (ICSITech)  (pp. 690–695). IEEE. https://doi.org/10.1109/icsitech.2017.8257201

Priyaadharshini, M., Dakshina, R., & Sandhya, S. (2020). Learning analytics: Game-based learning for programming course in higher education. Procedia Computer Science, 172 , 468–472. https://doi.org/10.1016/j.procs.2020.05.143

Qian, M., & Clark, K. R. (2016). Game-based Learning and 21st century skills: A review of recent research. Computers in Human Behavior, 63 , 50–58. https://doi.org/10.1016/j.chb.2016.05.023

Rajeev, S., & Sharma, S. (2018). Educational game-theme based instructional module for teaching introductory programming. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (pp. 3039–3044). IEEE. https://doi.org/10.1109/IECON.2018.8592835

Riera, B., Philippot, A., & Annebicque, D. (2019). Teaching the first and only logic control course with HOME I/O and Scratch 2.0. IFAC-PapersOnLine, 52 (9), 109–114. https://doi.org/10.1016/j.ifacol.2019.08.133

Roodt, S., & Ryklief, Y. (2022). Using digital game-based learning to improve the academic efficiency of vocational education students. In Research Anthology on Vocational Education and Preparing Future Workers (pp. 643–671). IGI Global. https://doi.org/10.4018/978-1-6684-5696-5.ch037

Roussou, E., & Rangoussi, M. (2020). On the use of robotics for the development of computational thinking in kindergarten: Educational intervention and evaluation. In M. Merdan, W. Lepuschitz, G. Koppensteiner, R. Balogh, & D. Obdržálek (Eds.), Robotics in Education (pp. 31–44). Springer. https://doi.org/10.1007/978-3-030-26945-6_3

Rozali, N. F., & Zaid, N. M. (2017). Code puzzle: ActionScript 2.0 learning application based on problem-based learning approach. In 2017 6th ICT International Student Project Conference (ICT-ISPC) (pp. 1–4). IEEE. https://doi.org/10.1109/ICT-ISPC.2017.8075329

Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30 , 344–360. https://doi.org/10.1007/s11031-006-9051-8

Sáiz Manzanares, M. C., Rodríguez Arribas, S., Pardo Aguilar, C., & Queiruga Dios, M. A. (2020). Effectiveness of self-regulation and serious games for learning STEM knowledge in primary education. Psicothema, 32 (4), 516–524. https://doi.org/10.7334/psicothema2020.30

Sánchez Mena, A., & Martí Parreño, J. (2017). Teachers´ acceptance of educational video games: A comprehensive literature review. Journal of E-Learning and Knowledge Society, 13 (2), 47–63. https://doi.org/10.20368/1971-8829/1319

Schatten, M., & Schatten, M. (2020). A comparative study of gamification in progamming education in a Croatian high school. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 700–704). IEEE. https://doi.org/10.23919/MIPRO48935.2020.9245237

Scherer, R., Siddiq, F., & Viveros, B. S. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior, 109 , 106349. https://doi.org/10.1016/j.chb.2020.106349

Seebauer, S., Jahn, S., & Mottok, J. (2020). Learning from Escape rooms? A study design concept measuring the effect of a cryptography educational Escape room. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1684–1685). IEEE. https://doi.org/10.1109/EDUCON45650.2020.9125333

Seralidou, E., & Douligeris, C. (2020). Creating and using digital games for learning in elementary and secondary education. In 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1–8). IEEE. https://doi.org/10.1109/SEEDA-CECNSM49515.2020.9221799

Serrano, K. (2019). The effect of digital game-based learning on student learning: A literature review. Graduate Research Papers , 943. https://scholarworks.uni.edu/grp/943

Shabalina, O., Malliarakis, C., Tomos, F., & Mozelius, P. (2017). Game-based learning for learning to program: from learning through play to learning through game development. In  11th European Conference on Games Based Learning 2017, Graz, Austria, 5–6 October 2017  (Vol. 11, pp. 571–576). Academic Conferences and Publishing International Limited.

Shahid, M., Wajid, A., Haq, K. U., Saleem, I., & Shujja, A. H. (2019). A Review of gamification for learning programming fundamental. In 2019 International Conference on Innovative Computing (ICIC) (pp. 1–8). IEEE. https://doi.org/10.1109/ICIC48496.2019.8966685

Sharaf, N., Ahmed, G., & Ihab, S. (2020). Virtual/mixed reality control of a game through Scratch. In 2020 24th International Conference Information Visualisation (IV) (pp. 689–693). IEEE. https://doi.org/10.1109/IV51561.2020.00119

Sharma, K., Papavlasopoulou, S., & Giannakos, M. (2019). Coding games and robots to enhance computational thinking: How collaboration and engagement moderate children’s attitudes? International Journal of Child-Computer Interaction, 21 , 65–76. https://doi.org/10.1016/j.ijcci.2019.04.004

Sharma, K., Torrado, J. C., Gómez, J., & Jaccheri, L. (2021). Improving girls’ perception of computer science as a viable career option through game playing and design: Lessons from a systematic literature review. Entertainment Computing, 36 , 100387. https://doi.org/10.1016/j.entcom.2020.100387

Shim, J., Kwon, D., & Lee, W. (2017). The effects of a robot game environment on computer programming education for elementary school students. IEEE Transactions on Education, 60 (2), 164–172. https://doi.org/10.1109/TE.2016.2622227

Siakavaras, I., Papastergiou, M., & Comoutos, N. (2018). Mobile games in computer science education: Current state and proposal of a mobile game design that incorporates physical activity. In T. A. Mikropoulos (Ed.), Research on e-Learning and ICT in Education (pp. 243–255). Springer. https://doi.org/10.1007/978-3-319-95059-4_15

Silva, J. P., Silveira, I. F., Kamimura, L., & Barboza, A. T. (2020). Turing project: An open educational game to teach and learn programming logic. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1–6). IEEE. https://doi.org/10.23919/CISTI49556.2020.9141122

Simões Gomes, T. C., Pontual Falcão, T., de Azevedo, C., & Restelli Tedesco, P. (2018). Exploring an approach based on digital games for teaching programming concepts to young children. International Journal of Child-Computer Interaction, 16 , 77–84. https://doi.org/10.1016/j.ijcci.2017.12.005

Sookhanaphibarn, K., & Choensawat, W. (2020). Educational games for cybersecurity awareness. In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) (pp. 424–428). IEEE. https://doi.org/10.1109/GCCE50665.2020.9291723

Stigall, J., & Sharma, S. (2017). Virtual reality instructional modules for introductory programming courses. In 2017 IEEE Integrated STEM Education Conference (ISEC) (pp. 34–42). IEEE. https://doi.org/10.1109/ISECon.2017.7910245

Sung, H. Y., & Hwang, G. J. (2013). A collaborative game-based learning approach to improving students’ learning performance in science courses. Computers & Education, 63 , 43–51. https://doi.org/10.1016/j.compedu.2012.11.019

Tabuti, L. M., de Azevedo da Rocha, R. L., & Nakamura, R. (2020). Proposal of method for converting a physical card game to digital for logical reasoning competencies on the data structure subject. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE44824.2020.9274001

Tacouri, H., & Nagowah, L. (2021). Code Saga – A mobile serious game for learning programming. In 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) (pp. 190–195). IEEE. https://doi.org/10.1109/IoTaIS53735.2021.9628484

Tang, T., Vezzani, V., & Eriksson, V. (2020). Developing critical thinking, collective creativity skills and problem solving through playful design jams. Thinking Skills and Creativity, 37 , 100696. https://doi.org/10.1016/j.tsc.2020.100696

Taylor, S., Min, W., Mott, B., Emerson, A., Smith, A., Wiebe, E., & Lester, J. (2019). Position: IntelliBlox: A toolkit for integrating block-based programming into game-based learning environments. In 2019 IEEE Blocks and Beyond Workshop (B&B) (pp. 55–58). IEEE. https://doi.org/10.1109/BB48857.2019.8941222

Tioh, J. N., Mina, M., & Jacobson, D. W. (2017). Cyber security training a survey of serious games in cyber security. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). IEEE. https://doi.org/10.1109/FIE.2017.8190712

Toh, W., & Kirschner, D. (2020). Self-directed learning in video games, affordances and pedagogical implications for teaching and learning. Computers & Education, 154 , 103912. https://doi.org/10.1016/j.compedu.2020.103912

Towler, A., Aranda, D., Ramyaa, R., & Kuo, R. (2020). Using educational game for engaging students in learning foundational concepts of propositional logic. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (pp. 208–209). IEEE. https://doi.org/10.1109/ICALT49669.2020.00067

Tretinjak, M. F. (2019). Methodology for using games as an educational tool. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 547–551). IEEE. https://doi.org/10.23919/MIPRO.2019.8757046

Tsur, M., & Rusk, N. (2018). Scratch microworlds: designing project-based introductions to coding. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 894–899). https://doi.org/10.1145/3159450.3159559

Turan, Z., Avinc, Z., Kara, K., & Goktas, Y. (2016). Gamification and education: Achievements, cognitive loads, and views of students. International Journal of Emerging Technologies in Learning (IJET), 11 (07), 64. https://doi.org/10.3991/ijet.v11i07.5455

Unger, S., & Meiran, W. R. (2020). Student attitudes towards online education during the COVID-19 viral outbreak of 2020: Distance learning in a time of social distance. International Journal of Technology in Education and Science, 4 (4), 256–266. https://doi.org/10.46328/ijtes.v4i4.107

Vahldick, A., Farah, P. R., Marcelino, M. J., & Mendes, A. J. (2020). A blocks-based serious game to support introductory computer programming in undergraduate education. Computers in Human Behavior Reports, 2 , 100037. https://doi.org/10.1016/j.chbr.2020.100037

Valle, P. H. D., Toda, A. M., Barbosa, E. F., & Maldonado, J. C. (2017). Educational games: A contribution to software testing education. In  2017 IEEE Frontiers in education Conference (FIE)  (pp. 1–8). IEEE. https://doi.org/10.1109/FIE.2017.8190470

Vankúš, P. (2021). Influence of game-based learning in mathematics education on students’ affective domain: A systematic review. Mathematics, 9 (9), 986. https://doi.org/10.3390/math9090986

Venkatesh, P., Das, S., & Das, A. K. (2021). Design and development of low-cost unplugged activities for teaching computational thinking at K-5 level. In  Design for Tomorrow—Volume 3  (pp. 523–534). Springer, Singapore. https://doi.org/10.1007/978-981-16-0084-5_42

Ventura, J., Qualls, J., Ventura, M., McGinnis, R., Baker, C., & Nikaido, B. (2017). Development of a video game design program in the college of engineering. In SoutheastCon 2017 (pp. 1–7). IEEE. https://doi.org/10.1109/SECON.2017.7925397

Videnovik, M., Kionig, L., Vold, T., & Trajkovik, V. (2018). Testing framework for investigating learning outcome from quiz game: A Study from Macedonia and Norway. In 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1–5). IEEE. https://doi.org/10.1109/ITHET.2018.8424777

Videnovik, M., Trajkovik, V., Kiønig, L. V., & Vold, T. (2020). Increasing quality of learning experience using augmented reality educational games. Multimedia Tools and Applications, 79 (33–34), 23861–23885. https://doi.org/10.1007/s11042-020-09046-7

Videnovik, M., Vold, T., Dimova, G., Kiønig, L. V., & Trajkovik, V. (2022). Migration of an Escape room–style educational game to an online environment: Design Thinking methodology. JMIR Serious Games, 10 (3), e32095. https://doi.org/10.2196/32095

Visoottiviseth, V., Phungphat, A., Puttawong, N., Chantaraumporn, P., & Haga, J. (2018). Lord of Secure: The virtual reality game for educating network security. In 2018 Seventh ICT International Student Project Conference (ICT-ISPC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICT-ISPC.2018.8523947

Vlahu-Gjorgievska, E., Videnovik, M., & Trajkovik, V. (2018). Computational thinking and coding subject in primary schools: Methodological approach based on alternative cooperative and individual learning cycles. In  2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)  (pp. 77–83). IEEE. https://doi.org/10.1109/tale.2018.8615334

Vostinar, P. (2021). MakeCode Arcade: Interesting environment for programming 2D games. In 2021 IEEE World Conference on Engineering Education (EDUNINE) (pp. 1–6). IEEE. https://doi.org/10.1109/EDUNINE51952.2021.9429132

Vourletsis, I., & Politis, P. (2022). Exploring the effect of remixing stories and games on the development of students’ computational thinking. Computers and Education Open, 3 , 100069. https://doi.org/10.1016/j.caeo.2021.100069

Vu, P., & Feinstein, S. (2017). An exploratory multiple case study about using game-based Learning in STEM classrooms. International Journal of Research in Education and Science . https://doi.org/10.21890/ijres.328087

Wang, L. H., Chen, B., Hwang, G. J., Guan, J. Q., & Wang, Y. Q. (2022). Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education, 9 (1), 1–13. https://doi.org/10.1186/s40594-022-00344-0

Webster, J. J., & Kit, C. (1992). Tokenization as the initial phase in NLP. In Proceedings of the 14th Conference on Computational Linguistics , (Vol. 4, pp. 1106–1110). https://doi.org/10.3115/992424.992434

Wong, Y. S., Hayati, I. M., Yatim, M., & Hoe, T. W. (2017). A propriety game-based learning mobile game to learn object-oriented programming—Odyssey of Phoenix. In 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 426–431). IEEE. https://doi.org/10.1109/TALE.2017.8252373

Wong, G. K. W., & Jiang, S. (2018). Computational thinking education for children: Algorithmic thinking and debugging. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 328–334). https://doi.org/10.1109/TALE.2018.8615232

Wong, Y. S., & Yatim, M. H. M. (2018). A propriety multiplatform game-based learning game to learn object-oriented programming. In 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 278–283). IEEE. https://doi.org/10.1109/IIAI-AAI.2018.00060

Workman, M. D., Luevanos, J. A., & Mai, B. (2021). A study of cybersecurity education using a Present-Test-Practice-Assess model. IEEE Transactions on Education, 65 (1), 40–45. https://doi.org/10.1109/TE.2021.3086025

Xian, T. (2021). Exploring the effectiveness of Sandbox game-based learning environment for game design course in higher education. In T. Ahram, R. Taiar, & F. Groff (Eds.), Human interaction, emerging technologies and future applications IV (pp. 262–272). Cham: Springer. https://doi.org/10.1007/978-3-030-74009-2_33

Yallihep, M., & Kutlu, B. (2020). Mobile serious games: Effects on students’ understanding of programming concepts and attitudes towards information technology. Education and Information Technologies, 25 (2), 1237–1254. https://doi.org/10.1007/s10639-019-10008-2

Yassine, A., Berrada, M., Tahiri, A., & Chenouni, D. (2018). A cross-platform mobile application for learning programming basics. International Journal of Interactive Mobile Technologies (IJIM), 12 (7), 139. https://doi.org/10.3991/ijim.v12i7.9442

Yokoyama, H., Fukui, M., Matsumoto, T., Mori, Y., & Takami, T. (2020). Use gameplay RPA as programming materials. In 2020 Nicograph International (NicoInt) (pp. 92–92). IEEE. https://doi.org/10.1109/NicoInt50878.2020.00028

Yongqiang, C., Xiaojun, W., & Chengbin, Q. (2018). Computer programming education for primary school students. In 2018 13th International Conference on Computer Science & Education (ICCSE) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCSE.2018.8468824

Yu, Z. (2019). A meta-analysis of use of serious games in education over a decade. International Journal of Computer Games Technology, 2019 , 1–8. https://doi.org/10.1155/2019/4797032

Yücel, Y., & Rızvanoğlu, K. (2019). Battling gender stereotypes: A user study of a code-learning game, “Code Combat” with middle school children. Computers in Human Behavior, 99 , 352–365. https://doi.org/10.1016/j.chb.2019.05.029

Zapata-Caceres, M., & Martin-Barroso, E. (2021). Applying game learning analytics to a voluntary video game: Intrinsic motivation, persistence, and rewards in learning to program at an early ge. IEEE Access, 9 , 123588–123602. https://doi.org/10.1109/ACCESS.2021.3110475

Zapušek, M., & Rugelj, J. (2013). Learning programming with serious games. EAI Endorsed Transactions on Game-Based Learning, 1 (1), e6. https://doi.org/10.4108/trans.gbl.01-06.2013.e6

Zaw, H. H., & Hlaing, S. Z. (2020). Verifying the gaming strategy of self-learning game by using PRISM-games. In P. Vasant, I. Zelinka, & G.-W. Weber (Eds.), Intelligent computing and optimization (pp. 148–159). Springer. https://doi.org/10.1007/978-3-030-33585-4_15

Zdravevski, E., Lameski, P., Trajkovik, V., Chorbev, I., Goleva, R., Pombo, N., & Garcia, N. M. (2019). Automation in systematic, scoping and rapid reviews by an NLP toolkit: A case study in enhanced living environments. In I. Ganchev, N. M. Garcia, C. Dobre, C. X. Mavromoustakis, & R. Goleva (Eds.), Enhanced living environments (Vol. 11369, pp. 1–18). Springer. https://doi.org/10.1007/978-3-030-10752-9_1

Zeevaarders, A., & Aivaloglou, E. (2021). Exploring the programming concepts practiced by Scratch users: An analysis of project repositories. In 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 1287–1295). IEEE. https://doi.org/10.1109/EDUCON46332.2021.9453973

Zhang, F., Kaufman, D., & Fraser, S. (2014). Using video games in computer science education. European Scientific Journal, 10 (22), 37–52. https://doi.org/10.19044/esj.2014.v10n22p%25p

Zhao, D., Muntean, C. H., Chis, A. E., & Muntean, G.-M. (2021). Learner attitude, educational background, and gender influence on knowledge gain in a serious games-enhanced programming course. IEEE Transactions on Education, 64 (3), 308–316. https://doi.org/10.1109/TE.2020.3044174

Download references

Acknowledgements

Not applicable.

No funding was received for conducting this study.

Author information

Authors and affiliations.

Center for Innovations and Digital Education Dig-Ed, Solunska glava br.3, 1000, Skopje, North Macedonia

Maja Videnovik

Department of Business Administration and Organizational Studies, Inland Norway University of Applied Sciences, Holsetgata 31, 2318, Hamar, Norway

Tone Vold & Linda Kiønig

Faculty of Computer Science and Engineering, Ss Cyril and Methodius University in Skopje, Rugjer Boshkovikj 16, 1000, Skopje, North Macedonia

Ana Madevska Bogdanova & Vladimir Trajkovik

You can also search for this author in PubMed   Google Scholar

Contributions

VT and AMB had the idea for the article. TV defined the methodological steps. MV and AMB performed the literature search and data analyses. Data analyses were supported by VT and LK. MV drafted the article, while TV critically revised the work.

Corresponding author

Correspondence to Tone Vold .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Videnovik, M., Vold, T., Kiønig, L. et al. Game-based learning in computer science education: a scoping literature review. IJ STEM Ed 10 , 54 (2023). https://doi.org/10.1186/s40594-023-00447-2

Download citation

Received : 27 March 2023

Accepted : 18 August 2023

Published : 06 September 2023

DOI : https://doi.org/10.1186/s40594-023-00447-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Scoping review
• Game-based learning
• Educational games
• Computer science
• Computer science education

Our mission is to advance K-12 computer science (CS) education for all children by enabling and disseminating exemplary evidence-driven research, with a focus on identifying culturally relevant promising practices and transforming student learning.

Evaluation Instruments

We host over 120 instruments for evaluating self-efficacy, grit, knowledge, and more in computing education.

Research Briefs

We provide a set of research briefs that can be used when planning, implementing, and reporting on research studies.

Article Summaries

We have manually curated over 40 variables related to articles, including type of study, number of participants, and student grade levels.

Our Services

We design, conduct, promote, support, and disseminate research that K-12 educators find relevant and actionable. Find out how we can assist you with your next project.

Our Sponsors & Partners

Privacy Overview

• Advisers & Contacts
• Bachelor of Arts & Bachelor of Science in Engineering
• Prerequisites
• Declaring Computer Science for AB Students
• Declaring Computer Science for BSE Students
• Class of '25, '26 & '27 - Departmental Requirements
• Class of 2024 - Departmental Requirements
• COS126 Information
• Important Steps and Deadlines
• Independent Work Seminars
• Guidelines and Useful Information

Undergraduate Research Topics

• AB Junior Research Workshops
• Undergraduate Program FAQ
• How to Enroll
• Requirements
• Certificate Program FAQ
• Interdepartmental Committee
• Minor Program
• Funding for Student Group Activities
• Mailing Lists and Policies
• Study Abroad
• Jobs & Careers
• Admissions Requirements
• Breadth Requirements
• Pre-FPO Checklist
• FPO Checklist
• M.S.E. Track
• M.Eng. Track
• Departmental Internship Policy (for Master's students)
• General Examination
• Fellowship Opportunities
• Travel Reimbursement Policy
• Communication Skills
• Course Schedule
• Course Catalog
• Research Areas
• Interdisciplinary Programs
• Technical Reports
• Computing Facilities
• Researchers
• Technical Staff
• Administrative Staff
• Graduate Students
• Undergraduate Students
• Graduate Alumni
• Climate and Inclusion Committee
• Resources for Undergraduate & Graduate Students
• Outreach Initiatives
• Resources for Faculty & Staff
• Spotlight Stories
• Job Openings
• Undergraduate Program
• Independent Work & Theses

Suggested Undergraduate Research Topics

How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2024-2025

• Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
• Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
• Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
• Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

• Machine learning driven design
• Generative models for structured discrete objects
• Approximate inference in probabilistic models
• Accelerating solutions to partial differential equations
• Innovative uses of automatic differentiation
• Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

• Research Areas: Formal methods, programming languages, compilers, computer security.
• Software verification (for which taking COS 326 / COS 510 is helpful preparation)
• Game theory of poker or other games (for which COS 217 / 226 are helpful)
• Computer game-playing programs (for which COS 217 / 226)
•  Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

• Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
• Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487). 
• Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages. 
• Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
• Any topic in theoretical computer science.

David August, Room 221

Not available for IW or thesis advising, 2024-2025

• Research Areas: Computer Architecture, Compilers, Parallelism
• Containment-based approaches to security:  We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system.   Let's explore ways to use containment to solve real problems.  Expect to work with corporate security and technology decision-makers.
• Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures.  Let's find ways to realize this parallelism.
• Any other interesting topic in computer architecture or compilers. 

Mark Braverman, 194 Nassau St., Room 231

• Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
• Topics in computational and communication complexity.
• Applications of information theory in complexity theory.
• Algorithms for problems under real-life assumptions.
• Game theory, network effects
• Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

• Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
• Methods for collaborative and continual learning.
• Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

• Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms. 
• Natural algorithms (flocking, swarming, social networks, etc).
• Sublinear algorithms
• Self-improving algorithms
• Markov data structures

Danqi Chen, Room 412

• My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
• Representation learning for text and knowledge bases
• Pre-training and transfer learning
• Question answering and reading comprehension
• Information extraction
• Text summarization
• Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

• Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)
• Research Areas: Machine learning

Jia Deng, Room 423

•  Research Areas: Computer Vision, Machine Learning.
• Object recognition and action recognition
• Deep Learning, autoML, meta-learning
• Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

• Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

• Research Areas:  Software engineering; software engineering education.
• Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
• In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

• Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
• Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

• Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
• Using RL algorithms to applications in science and engineering.
• Emergent behavior of RL algorithms on high-fidelity robotic simulators.
• Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

• Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
• Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
• Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
• Quantitative approaches to theoretical linguistics questions
• Extensions and interfaces for WordNet (English and WN in other languages),
• Applications of WordNet(s), including:
• Foreign language tutoring systems,
• Spelling correction software,
• Word-finding/suggestion software for ordinary users and people with memory problems,
• Machine Translation 
• Sentiment and Opinion detection
• Automatic reasoning and inferencing
• Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

• Networking and telecommunications
• Learning, perception, and intelligence, artificial and otherwise;
• Human-computer interaction and computer-supported cooperative work
• Online education, especially in Computer Science Education
• Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
• Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308 

• Research Areas: Distributed systems, security, networking
• Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

• Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
• Develop a technique for understanding AI models
• Design a AI model that is interpretable by design
• Build a paradigm for detecting and/or correcting failure points in an AI model
• Analyze an existing AI model and/or dataset to better understand its failure points
• Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
• Develop a software package for explainable AI
• Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

• Research Areas: Formal methods, program analysis, logic decision procedures
• Finding bugs in open source software using automatic verification tools
• Software verification (program analysis, model checking, test generation)
• Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409  

• Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
• Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
• Implementation and algorithm engineering for control, reinforcement learning and robotics
• Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

• Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
• Optical Neural Networks
• Hardware-in-the-loop Holography
• Zero-shot and Simulation-only Learning
• Object recognition in extreme conditions
• 3D Scene Representations for View Generation and Inverse Problems
• Long-range Imaging in Scattering Media
• Hardware-in-the-loop Illumination and Sensor Optimization
• Inverse Lidar Design
• Phase Retrieval Algorithms
• Proximal Algorithms for Learning and Inference
• Domain-Specific Language for Optics Design

Peter Henderson , 302 Sherrerd Hall

• Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

• Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
• See other topics on my independent work  ideas page  (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

• Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
• Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
• Software visualization tools for education
• Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

• Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
• Application-oriented languages, scripting languages.
• Tools; user interfaces
• Digital humanities

Zachary Kincaid, Room 219

• Research areas: programming languages, program analysis, program verification, automated reasoning
• Independent Research Topics:
• Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
• Design a domain-specific programming language, or prototype a new feature for an existing language.
• Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

• Research area: theory

Aleksandra Korolova, 309 Sherrerd Hall

• Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Pravesh Kothari, Room 320

• Research areas: Theory

Amit Levy, Room 307

• Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
• Distributed hardware testing infrastructure
• Second factor security tokens
• Low-power wireless network protocol implementation
• USB device driver implementation

Kai Li, Room 321

• Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
• Fast communication mechanisms for heterogeneous clusters.
• Approximate nearest-neighbor search for high dimensional data.
• Data analysis and prediction of in-patient medical data.
• Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

• Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
• Explore new statistical retrieval models for document retrieval and question answering.
• Apply AI in various fields.
• Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
• Any interesting project related to AI, machine learning, and data analysis.

Lydia Liu, Room 414

• Research Areas: algorithmic decision making, machine learning and society
• Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
• Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
• Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
• Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

Wyatt Lloyd, Room 323

• Research areas: Distributed Systems
• Caching algorithms and implementations
• Storage systems
• Distributed transaction algorithms and implementations

Alex Lombardi , Room 312

• Research Areas: Theory

Margaret Martonosi, Room 208

• Quantum Computing research, particularly related to architecture and compiler issues for QC.
• Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
• Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
• Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307 

Available for Spring 2025 single-semester IW, only

• Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
• Assessing the effects of government policies, both in the public and private sectors.
• Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
• Developing new tools to improve government processes and offer policy alternatives.

Mae Milano, Room 307

• Local-first / peer-to-peer systems
• Wide-ares storage systems
• Consistency and protocol design
• Type-safe concurrency
• Language design
• Gradual typing
• Domain-specific languages
• Languages for distributed systems

Andrés Monroy-Hernández, Room 405

• Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
• Research interests:developing public-interest socio-technical systems.  We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems.  We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
• Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

• Research areas: Distributed systems, high-throughput computing, computer science/engineering education
• Expansion, improvement, and evaluation of open-source distributed computing software.
• Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
• Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
• Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

• Research areas: Natural Language Processing, Reinforcement Learning
• Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
• Transfer learning/generalization in NLP
• Techniques for generating natural language
• Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall 

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

 * Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

 1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in:   * Explicit constructions of better vertex expanders and/or unique neighbor expanders.   * Construction deterministic or random high dimensional expanders.   * Pseudorandom generators for different problems.   * Topics around the quantum PCP conjecture.   * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

 2. Theory informed practical implementations of algorithms   Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest:   * Streaming algorithms.   * Numeric linear algebra.   * Property testing.   * Parallel / Distributed algorithms.   * Online algorithms.    3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

• Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
• Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
• Possible research topics include:

1.   Quantum algorithms and circuits:

• i. Design or simulation quantum circuits implementing quantum algorithms.
• ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2.   Information Based Complexity:

• i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case). 
• ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
• iii. Necessary and sufficient conditions for tractability of Weighted problems.
• iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

• i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

• Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
• Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309  

• Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
• Implementation and application of algorithms to infer evolutionary processes in cancer
• Identifying correlations between combinations of genomic mutations in human and cancer genomes
• Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
• Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

• Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
• Constructing a new method to explain a model / create an interpretable by design model
• Analyzing a current model / dataset to understand bias within the model/dataset
• Proposing new fairness evaluations
• Proposing new methods to train to improve fairness
• Developing synthetic datasets for fairness / interpretability benchmarks
• Understanding robustness of models

Ran Raz, Room 240

• Research Area: Computational Complexity
• Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

• Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
• Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
• Investigate approaches to robust 3D scan matching
• Model and compensate for imperfections in 3D printing
• Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

• Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
• Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
• Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
• Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

• Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
• Gamification of neuroscience (EyeWire  2.0)
• Semantic segmentation and object detection in brain images from microscopy
• Computational analysis of brain structure and function
• Neural network theories of brain function

Jaswinder Pal Singh, Room 324

• Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
• Develop a startup company idea, and build a plan/prototype for it.
• Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
• Study and develop methods to infer insights from data in different application areas, from science to search to finance to others. 
• Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
• Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

• Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
• Whole and cross-genome methods for predicting protein function and protein-protein interactions.
• Analysis and prediction of biological networks.
• Computational methods for inferring specific aspects of protein structure from protein sequence data.
• Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

• Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
• Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
• Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

• Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
• Implement and evaluate one or more gene expression analysis algorithm.
• Develop algorithms for assessment of performance of genomic analysis methods.
• Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

• Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
• Independent Research Topics:  Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

• Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

• Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
• Design and implement computer visualizations of algorithms or data structures.
• Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
• Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

• Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
• Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc. 
• Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

• data structures
• streaming algorithms
• design and analyze data structures / streaming algorithms
• prove impossibility results (lower bounds)
• implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

Opportunities outside the department.

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science.  However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.)  To do a project with an adviser outside of computer science you must have permission of the department.  This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

• Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

• Documentation of historic structures
• Cyber physical systems for structural health monitoring
• Developing virtual and augmented reality applications for documenting structures
• Applying machine learning techniques to generate 3D models from 2D plans of buildings
•  Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

• Internet security and privacy 
• Social Networks
• Privacy technologies, anonymous communication
• Network Science
• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman,  Psychology Dept, PNI 137

• Research Areas: Memory, the brain and computation 
• Lab:  Princeton Computational Memory Lab

Potential research topics

• Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG) 
• Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The  Campus as Lab  program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its  website .

An example from Computer Science could include using  TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a  live energy heatmap of campus .

Other potential projects include:

• Apply game theory to sustainability challenges
• Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
• How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

• Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
• Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

• Instrument Princeton's Green (HPCRC) data center
• Investigate power utilization on an processor core implemented in an FPGA
• Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
• Other topics in parallel computer architecture or operating systems

Exploring the state of computer science education amid rapid policy expansion

Subscribe to the brown center on education policy newsletter, michael hansen and michael hansen senior fellow - brown center on education policy , the herman and george r. brown chair - governance studies @drmikehansen nicolas zerbino nicolas zerbino senior research analyst.

April 11, 2022

• 35 min read

The role of computers in daily life and the economy grows yearly, and that trend is only expected to continue for the foreseeable future. Those who learn and master computer science (CS) skills are widely expected to enjoy increased employment opportunities and more flexibility in their futures, though the U.S. currently produces too few specialists to meet future employment demands. Thus, providing exposure to CS during compulsory schooling years is believed to be key to maintaining economic growth, increasing employment outcomes for individuals, and reducing historical gaps in participation in technology fields by gender and race. Consequently, providing young people with access to quality CS education is increasingly seen as an urgent priority for public school systems in the U.S. and around the globe .

Primary objectives of CS education, as described in the “K-12 Computer Science Framework”—a guiding document assembled by several CS and STEM education groups in collaboration with school leaders across the country—are to help students “develop as learners, users, and creators of computer science knowledge and artifacts” ( p. 10 ) and to understand the general role of computing in society. CS skills enable individuals to understand how technology works and how best to harness its potential in their personal and professional lives. CS education is distinct from digital literacy as it is primarily concerned with computer design and operations, rather than the simple use of computer software. Common occupations that heavily utilize CS skills include software engineers, data scientists, and computer network managers; however, as described below, CS skills are becoming more integral to many occupations in the economy beyond technology fields.

The past decade has been an active period of policy expansion in CS education across states and growing student engagement in CS courses. Yet, little is known about how policies may have influenced student outcomes. This report offers a first look at the relationship between recent policy changes and participation, as well as pass rates on the Advanced Placement Computer Science (AP CS) exams.

Based on our analysis looking over the last decade, we present five key findings:

• We observe sharp, coinciding increases in both state adoption of CS education policies and overall participation in AP CS exams.
• AP CS participation rates for all student subgroups have also increased, with representation gaps between student groups narrowing.
• Narrowing participation gaps for females and especially Black and Latino students have been primarily driven by the introduction of a new AP CS exam (CS Principles), with gaps changing little since then.
• Passing rates on AP CS exams have modestly increased for underrepresented student groups during this period, resulting in slightly narrower passing gaps.
• AP CS student participation overall is associated with increased CS policy adoption, though participation gaps between over- and underrepresented groups appear to be uncorrelated with recent policy adoptions.

Providing universal access to CS education

CS education is undergoing an important transformation in schools. Classes in computing and CS have long been offered in K-12 public schools, though have not been uniformly required, nor universally available. Thus, access to CS has been uneven across student populations. Yet, the growing importance of technological and computing skills in modern society has compelled many school systems to adopt policies to provide universal access to CS education. Several reasons often motivate this expanded access.

First, expanding CS education is expected to directly benefit students. Individuals who develop expertise in computer and technology fields enjoy higher wages and employment. Even those who do not pursue technical occupations still reap these benefits , as computing and data analysis skills have been broadly integrated into many industries and occupations. Finally, CS education also benefits students who do not use computers in their future careers. Prior studies have documented cognitive and interpersonal skills that CS education uniquely provides to students, which transfer outside of computing domains. Moreover, understanding CS fundamentals contributes valuable life skills that prepare and protect students for a future in which many aspects of daily life are carried out in digital contexts.

“The growing importance of technological and computing skills in modern society has compelled many school systems to adopt policies to provide universal access to computer science education.”

Next, economies overall fare better when individuals are more technologically competent. Studies show a positive relationship between economic growth, technology, and human-capital investments in related skills. Many states and countries view computing and technology jobs as engines of economic growth; thus, providing public school students with quality CS education enables sustainable growth. Federal and local politicians often appeal to this economic rationale to justify investments in CS education to public stakeholders— early CS policy-adopter Arkansas is a prime example.

And third, universal access to high-quality CS education is necessary to close historical gaps in technology fields. Black, Latino, and Indigenous populations and women have long been underrepresented in STEM occupations that heavily rely on CS and computing skills . Given the higher wages and job prospects associated with these fields, this underrepresentation of diverse populations in STEM implicitly contributes to race- and gender-based gaps along economic lines. Developing technical skills provides a path to upward social mobility, as has been shown through the assimilation experience of some immigrant groups : Those with computing and other STEM skills reach earnings parity with native workers far faster than those without these skills.

Prior research indicates low access to CS educational opportunities and resources being critical drivers of STEM participation gaps, which tend to mirror larger socioeconomic inequalities based on race, income, or locale. For example, when the only CS offering in a school is an extracurricular robotics club, only those with intrinsic motivation and the resources to participate will gain access to this learning opportunity. Lower access to CS could manifest in various ways from infrequent exposures to computer-based learning applications in the classroom to fewer courses being offered in high schools. Unequal access fails to explain gender-based participation gaps, though these are likely driven by other socialized gender norms that deter girls from computing and other STEM fields . Universal access, however, is expected to both provide CS skills to all students and stimulate greater engagement among underrepresented groups, increasing diversity in STEM occupations.

“Student access to computer science education is highly variable across the U.S.”

Student access to CS education is highly variable across the U.S. Though many schools have provided computer labs and classes in computer literacy (e.g., typing, internet use, word processing), CS courses go beyond basics to provide instruction on computational thinking and other digital operations, and they require teachers with these skills. In many places across the U.S., CS is only offered to students as elective courses or extracurricular activities , if at all. Leaving the provision of CS education to these voluntary contexts leaves the quality of the CS experience highly variable, and dependent on the availability of local resources. Universal access to CS education , however, is expected to standardize learning standards, augment local resource constraints, and ensure equal access to quality instruction.

Enacting CS education policy laws

Calls for universal CS education have been around for years—ranging from corporate efforts and nonprofit advocacy to federal awareness-raising events —though progress has been slow until very recently. Only since 2015 have these efforts yielded the critical mass to push many states to adopt sweeping change in support of CS education.

To illustrate this transformation, consider the policy changes documented through the annual “State of Computer Science Education” (State of CS) reports, co-authored by Code.org Advocacy Coalition, Computer Science Teachers Association, and Expanding Computing Education Pathways. Since 2017, the State of CS reports have promoted and tracked nine different policies intended to promote CS education in schools. 1 The nine policies are:

• whether the state has adopted a formal plan for CS education (abbreviated as State Plan for reporting);
• whether the state has implemented K-12 CS education standards (Standards);
• whether state-level funding is dedicated to CS programs (Funding);
• whether a CS teacher’s certification exists (Certification);
• whether a state-approved pre-service teacher-preparation program for future CS educators is provided at any higher education institutions (Pre-service);
• whether a state-level CS officer exists (State CS);
• whether all high schools are required to offer computer science (Require HS);
• whether a CS course can satisfy a core high school graduation requirement (Count); and,
• whether CS can satisfy a core admissions requirement at state colleges and universities (Higher ed).

In just five years, states showed a remarkable policy transformation; Figure 1 combines and animates this evolution. 2 In the 2017 report, Arkansas was the only state that had adopted at least seven of the nine tracked policies. Meanwhile, 36 states had adopted three or fewer policies, including nine states that had adopted no state-level CS policies at all. But in the 2021 report, 24 states had at least seven policies on the books—a remarkable shift observed across all geographical regions. Only 10 states remain in the lowest adoption category, and all states have adopted at least one policy.

Figure 1 also identifies which policies are adopted. The most commonly adopted policy is having a CS course satisfy a core high school graduation requirement, with all 50 states plus Washington, D.C., adopting it by 2021. Other popular policies include having a state CS plan, funding CS initiatives, creating a state-level CS officer, adopting K-12 CS standards, and recognizing a CS certification for teachers; each of these policy categories counts more than 30 states taking action in the area by 2021.

Providing universal access to CS education in many locales has typically followed the provision of (near) universal access to personal computing devices and broadband. Though some elements of CS fundamentals can be taught without the aid of computers and an internet connection, these are required inputs for a full CS curriculum . Historically, schools and households located in low-income or rural communities have had lower access to digital infrastructure—a phenomenon widely known as the digital divide. Aside from a host of other negative consequences , the implications of this divide on CS education is that students in these contexts have fewer opportunities to regularly interact with computing devices in learning contexts and will have less access to high-quality CS instruction.

More recently, however, the COVID-19 pandemic has acted as a catalyst in making real progress on closing the digital divide. Providing widespread access to needed computing resources has been an urgent priority for many school systems as they have worked to stay connected with students while schools were closed for extended periods. With new devices and ready access to the internet, previously disconnected students are beginning to regularly interact with computers to facilitate their learning. Thus, where some communities may have been less able to offer CS for these reasons in the past, we anticipate that hardware and infrastructure barriers should be less formidable moving forward.

More students are taking AP CS exams

In this active era of CS policy adoption, we explore whether these actions correspond to changes in students’ outcomes in CS. Are students more likely to participate and succeed in CS learning? Do race- and sex-based gaps reduce with more universal access?

To investigate these questions, we use state-level outcomes on the College Board’s AP exams in CS. AP exams are useful outcome measures for this investigation because they are standardized, administered nationally, and represent meaningful competencies in the field that are broadly recognized. This section provides background detail about the AP CS exams.

Situated at the transition point between high school and college, AP courses in multiple subjects are offered in most high schools to advanced students, typically in their final year(s) of high school. Students may opt to take the AP exam at the end of the school year to demonstrate their mastery of the course material. When students matriculate to college, many institutions will award those who passed an AP test with college credits corresponding to an introductory course in the field. Thus, participating in and passing an AP CS exam should probably be considered as a capstone student outcome; that is, one that is realized after multiple years of CS learning opportunities.

Students’ participation in AP courses and exams are widely perceived as important signals of college readiness, and many high schools have expanded their AP course offerings to signal rigor to parents and motivate students. Some scholars question the extent to which participation in AP classes genuinely increases students’ likelihood of college success (since it is primarily advanced students who are enrolling in these courses), and controlling for many student background characteristics sharply diminishes the apparent advantage to AP participation. Other evidence from incentive-driven expansions of AP courses in disadvantaged settings points to AP participation having a causal, positive impact on SAT/ACT scores and college enrollment. Though looking across many studies of the AP program, the academic benefits accrue almost exclusively to those who pass the AP exam (participating in the course without passing the exam provides little, if any, academic benefit).

“Socioeconomically disadvantaged groups lack equal access to AP programming in their schools.”

Even if only those who successfully pass the AP exam benefit, socioeconomically disadvantaged groups lack equal access to AP programming in their schools. In 2014, the Department of Education’s Office for Civil Rights conducted a special data collection on student access to advanced coursework. Reporting shows Black and Latino students account for 27% of those enrolled in at least one AP course and 18% of those passing at least one AP exam, despite these groups accounting for 37% of all students. Further, these gaps are not limited to AP courses but are also evident in advanced STEM courses (like algebra II and physics).

During the years of our investigation, the College Board administered two AP exams covering CS content: Computer Science A (AP CS A) and Computer Science Principles (AP CS P). AP CS A is intended to cover material expected of a first-year CS course in college (with a heavy emphasis on coding), while AP CS P is expected to cover a first-year computing course (including more foundational content such as technology’s impacts on society and understanding how algorithms and networks function). Students in both courses will learn to design a computer program, but only students taking AP CS A will develop the algorithms and code needed for implementation. This does not necessarily mean that AP CS A is more effective—though it is more rigorous and would come after AP CS P in a course sequence. A recent College Board report concludes that students who take AP CS P (relative to those not given the chance) are more likely to take AP CS A in later high school years or declare a CS college major. Though not causal, these findings underscore the importance of AP CS P in developing student interest in the field, particularly among underrepresented student groups.

Of the two exams, AP CS A has a longer history, tracing its origins back to 1984. For much of its history, a modest 20,000 or fewer students would take the exam annually, though these numbers have begun to expand in the last decade. The AP CS P exam, however, was introduced in the 2016-17 school year and has quickly surged in popularity. By spring 2018, its second year of administration, student demand for the AP CS P exam (62,868 public school students) had already surpassed demand for AP CS A (51,645 students).

Figure 2 presents the number of exams taken between 2012-2020 (the most recent year with data available). The first half of the series, AP CS A was the only AP CS exam offered and student demand grew modestly year to year. The AP CS P exam quickly dominated once introduced. In 2020, over 150,000 students took one of these AP CS exams, with nearly two-thirds of that demand coming from AP CS P. For reference, participation in AP exams overall has grown from over 950,000 students in 2012 to 1.21 million in 2020 (27% growth). The surging interest in AP CS exams has significantly outpaced general increases in the other AP subjects.

A recent comparative study of the two AP CS exams finds important differences between students, skill mastery, and intended occupational fields. Students who take the AP CS A exam frequently take several other AP exams and intend to pursue majors in either CS or other STEM fields once in college. Conversely, students taking the AP CS P exam only reported less interest in pursuing CS or STEM majors and careers, and they expressed lower computing confidence (as expected, given the more foundational material).

Further, students who took only the AP CS P were more diverse than those who took AP CS A, though underrepresentation for Black, Latino, and female students is still apparent in both exams. 3 Figure 3 illustrates the differences in diversity between the two AP CS exams. Like the preceding figure, it shows the recent time series of AP test-takers, though instead of numerical counts we are looking at the share of Black and Latino (light blue lines) or female (dark blue lines) test-takers on the y-axis. Black and Latino students constitute between 13-18% of AP CS A test-takers for the entire series but represent 28-30% of AP CS P test-takers. Similarly, female students grew from 18% of AP CS A test-takers in 2012 to 25% in 2020; they constituted an even greater share of AP CS P test-takers during the years it was administered (growing from 30% in 2017 to 34% in 2020).

Throughout the remainder of the report, we combine student results on both AP CS exams and report pooled statistics. We do this primarily for simplicity in reporting, as most outcomes show roughly redundant patterns when analyzed separately by exam; exceptions to this will be noted in the text.

Exploring AP CS outcomes by student race and sex

The AP CS exam results provide two discrete outcomes that we use in the remaining analysis: test-taking and passing. The College Board reports state-level statistics by year and student race and sex for both outcomes, and these will be linked to state policy changes that we described earlier. This section first investigates how the expansion of testing in AP CS evolved through the lens of race and sex representation.

Before proceeding, we should note an important limitation regarding the AP CS exam passing data: When small numbers of students are present in a reported cell, the College Board censors the cell to protect students’ privacy. Cell censoring is common in states with small populations when reporting is broken out by state, year, exam, and race or gender combinations. Consequently, we are constrained in our ability to investigate state policies and their association with passing outcomes by race and sex. We will report some passing rates as pertinent below, though much of the analysis that follows uses test-taking as the primary AP CS outcome.

As discussed previously, increasing racial and gender diversity in CS and related STEM fields is an important motivating factor in adopting universal CS education policies. Have narrowing gaps in AP CS test-taking and passing coincided with the expansion of state-level CS education policies?

Related Content

Michael Hansen, Emiliana Vegas, Fred Dews

November 19, 2021

Online only

10:00 am - 11:00 am EDT

Figure 4 illustrates how differences in representation on AP test-taking have evolved in recent years. The figure is comprised of two animated scatterplots that trace the differences in representation between overrepresented groups on the x-axis (males on the left, white and Asian students on the right) and underrepresented groups on the y-axis (females on the left, Black and Latino students on the right). On both axes are the state’s proportion of each student group represented among test-takers (referenced against the state’s population of 12 th -grade students). 4 Both panels have a 45-degree reference line, marking parity on AP CS test-taking between overrepresented and underrepresented groups. Points falling below this reference line represent test-taking gaps where whites, Asians, and males continue to be overrepresented. A line is also fitted across state observations—points lying on this line share the same relative proportions in the test-taking population between under- and overrepresented groups.

In 2012, the earliest year of the animation, all states are clustered into the bottom left-hand corner of the scatterplots. The position of these points shows low participation overall, and participation is especially low among Black, Latino, and female students. When play is pressed on the animation, the points shift away from the origins, though almost exclusively within the same halves of the plot areas southeast of the reference lines. The fitted line between state observations shows that representation gaps in test-taking have narrowed slightly with time (as the fitted line takes on a steeper slope, moving it closer to parity), though large gaps persist in most states.

Table 1 below provides two key metrics that help to describe how these test-taking patterns by student subgroups have evolved over time. The first metric is the ratio of participation gaps (underrepresented groups/overrepresented groups), which is essentially what the fitted lines in Figure 4 illustrate. A value of 1 represents parity between groups (just as the 45-degree line above has a slope of 1). Participation rates were more than four times higher among male 12 th graders compared to females in 2012, resulting in a participation ratio of 0.24. Increasing female participation in recent years has brought them closer to parity with a 2020 value of 0.46. Table 1 also reports the difference in the share of test-takers from overrepresented groups less underrepresented groups, where a value of 0 represents a 50-50 split in test-takers’ demographics. In 2012, AP CS test-takers were just under 20% female, and just over 80% male, resulting in a test-taking share gap exceeding 62 percentage points. This gap has narrowed to less than 40 percentage points as of 2020. Similar patterns of progress are shown on race-based metrics.

Table 1 shows both the participation ratios and test-taking share gaps calculated by sex and race for three selected years: the first year of data (2012), the year AP CS P was introduced (2017), and the final year (2020). Examining how these metrics have changed over the series is instructive: Much of the overall improvements in the metrics were realized in 2017 with the introduction of the AP CS P exam. Progress made in the years since has been more modest in comparison, and the gains have been larger on sex gaps rather than racial gaps.

We find other encouraging patterns of narrowing gaps when focusing on AP CS passing rates. When rapidly expanding the test-taking pool, one might be concerned that students who are induced to take the AP CS exams will not be as prepared for the exams as those students who had already prepared for AP CS before the expansion. This concern resonates especially for the AP CS P exam, which has expanded dramatically to more than 100,000 exams taken annually in just a few years. To the contrary, though, our analysis of the data suggests that passing rates among underrepresented groups have increased during this period of AP CS expansion and increased faster than those among overrepresented groups.

Figure 5 presents the passing rates on AP CS exams by sex (on the left) and race (on the right) over recent years. The x-axes represent years and the y-axes represent the passing rates for each student group; passing rates are pooled across both AP CS exams. In both panels, the overrepresented groups are passing the exams at higher rates, and an especially large margin is apparent between racial groups. Yet, during these years of participation growth, passing rates among underrepresented groups simultaneously increased. Meanwhile, the passing rates for overrepresented groups (males on the left, whites and Asians on the right) inched upward during this period of expansion. On net, the gaps between these groups narrowed, and female passing rates overtook that of males in 2020.

To confirm that the narrowing gaps depicted in Figure 5 are not simply driven by the surging popularity of the AP CS P exam, we separately investigated passing rates on each of the AP CS exams. The narrowing gaps observed in Figure 5 are also observed in each test. For example, female passing rates on the AP CS A exam increased from 56% (2012) to 68% (2020), and they increased on the AP CS P exam from 70% (2017) to 75% (2020). Increases of 5 or more percentage points were similarly observed among Black and Latino test-takers on both tests during this period. Meanwhile, the passing rates among overrepresented groups increased slightly on the AP CS A exam over the period, while dropping slightly on the AP CS P exam. Again, the net results showed narrowing gaps for underrepresented groups both by race and sex on both exams.

Associating CS education policy changes with AP test-taking

Finally, we explore whether states that are making more progress on their CS education policies show more favorable outcomes on AP CS exams. For example, it’s possible that those states taking more policy actions to improve universal access to CS education have seen greater uptakes in AP CS participation or sharper reductions in underrepresented gaps when compared with those states doing little.

Before discussing our results, though, we must acknowledge that policy adoption metrics are imperfect proxies for practice. The State of CS reports are careful to note that state policies vary widely, even within the same policy categories. Further, a state may decide to adopt a given CS education policy, but implementation may be thwarted by barriers that curtail its practical impact. Other states may put CS-enhancing practices into place even in the absence of a formalized state policy. This difficulty can be seen in Figure 6, which represents the differences in observed practices under three different policy-status categories. Figure 6 focuses on the percentage of high schools in a state offering foundational CS courses (y-axis), a practice that provides more universal access to CS for all students. The State of CS policy corresponding to this action is whether states have a policy requiring all high schools to offer CS (Require HS). The x-axis separates those states that have no policy, those that have adopted a policy with a target implementation goal in the future (in progress), and those with the policy already in force (yes).

The box-whisker plots represent the means and distributions of states observed within each of the three policy-status categories. Those states with a state policy in force have the highest mean percentage of high schools offering CS, and those with the policy in progress have higher percentages than states with no policy. Yet, the observed differences in practice across states are far smaller than the policy-status variables alone would indicate. The key point here is that we are constrained to look at the data available to us on policy status, not actual practices; consequently, we may be failing to capture important differences in practice in our analyses.

To conduct the analysis, we merged the State of CS policy adoption data with the AP CS exam data by state and year. 5 We ran a series of two-way fixed-effects models, which are intended to net out other correlated changes in test-taking behavior observed within the state over time and across other states contemporaneously. We ran a separate model on each of the nine tracked CS policies and looped this operation across different test-taking metrics as dependent variables. The results of this exercise are presented in Table 2 below.

The columns of Table 2 correspond to different analytical models in which the outcomes of interest are the overall test-taking rate (column 1) as well as the percentage of test-takers that are female (column 2) and Black or Latino (column 3). The nine CS policies are represented down the row headings. The cell corresponding to a row-column combination represents the point estimate and standard error of a two-way fixed-effects model with the policy in the row heading being used as the explanatory variable and the student group in the column heading as the output of interest. Cells are color coded for ease of interpretation to highlight where the estimates are largest.

The high-level summary of the Table 2 results is that several of these CS education policies are positively associated with AP CS test-taking behavior among students overall. The first column shows the largest and most statistically significant estimates correspond to policies that 1) allocate state funding for CS education initiatives, 2) require state colleges to recognize CS courses as STEM courses in admissions decisions, and 3) require all high schools in the state to offer CS courses. We are generally unsurprised at this result, as all three of these policies feasibly have a direct impact on late-high-school students, which are the target population for AP CS exams. Other policies, like offering a teacher certification program in CS education or having a state-level officer responsible for CS education, would likely influence these late-high-school outcomes through more indirect means.

Another finding from Table 2 is that none of the policies seem to be associated with a relative increase in the proportion of test-takers from underrepresented groups. Only one point estimate is significant in column 2 (whether a CS course counts toward a STEM graduation requirement), and it is in the direction of widening the sex-based gap. This result must be taken with a grain of salt because this policy (Count) was primarily adopted in the earlier years of the past decade when gaps were at their largest. A crucial factor driving these estimates is the (almost) constant proportion of underrepresented test-takers between 2018 and 2020, the years for which we have an overlap of policy implementation and AP test-taking data.

We should also note that with the high levels of state policy activity coinciding with a rapid expansion of AP CS test-taking, we cannot claim that any of the point estimates reported in Table 2 represent a causal relationship. Rather, this is our best attempt to isolate associations that are unique to certain policy-outcome combinations to explore the relationship; results are not intended to be definitive evaluations of any given policy.

Even if the expansion of these CS policies had little apparent relationship with test-taking gaps overall, this does not mean that that was the experience of students in all states. We wish to explore whether surges in the performance of underrepresented groups accompanied CS policy expansions in any state, and we do this in the map presented in Figure 7.

Figure 7 presents a bivariate map of the U.S., where states are color coded based on observed changes in two directions: growth in state-level CS education policy adoption and growth in Black and Latino AP CS test-taking rates. States above the median on both dimensions are shaded in dark blue, and states below the median on both are shaded in light gray. The light blue and dark gray shades represent states high on one dimension or the other, but not both.

This analysis reveals some surprising geographical differences. Using the Mississippi River as the dividing line, nearly all states with the highest increases in test-taking among Black and Latino student groups are east of the river (Nevada and Montana are the only exceptions west of the Mississippi). And among the states with the highest test-taking increases in the East, states are split about evenly between high and low policy-adoption categories. Contrast this pattern against states west of the Mississippi, where nearly all states are in the low-growth category for Black and Latino AP CS test-taking, with over two-thirds of those are in the low-growth policy category.

Reflecting on the map leaves us with two important lessons. First, the map vividly illustrates that policy adoption itself is not an accurate predictor of stronger outcomes for underrepresented groups. We observe many states with high policy growth that see comparably little improvement in test-taking outcomes for Black and Latino students; meanwhile, we also see many examples with high growth among Black and Latino students that did not display the same aggressive levels of policy adoption.

“Policy adoption itself is not an accurate predictor of stronger outcomes for underrepresented groups.”

And second, the map suggests that geographical commonalities may be an important lever supporting CS student outcomes. It is unclear from this analysis how those geographical relationships will matter, but this offers some useful direction for future work. A suggestive clue comes from the 2021 State of CS report ( p. 14 ), which shows a policy map of the percentage of schools offering foundational CS, with a similar East-West divide evident. We confirm that the percentage of high schools offering CS at the state level is also positively correlated with both our measure of policy growth and increasing Black and Latino participation. Though merely suggestive, more universal high school CS offerings presents a clear mechanism through which greater shares of underrepresented groups will be exposed to CS instruction, and therefore participate in meaningful coursework leading to AP CS exams.

Concluding discussion and recommendations

We investigated CS education policy adoption and AP CS exam outcomes in recent years—both of which saw rapid expansion during this time. We found gaps modestly narrowing for historically underrepresented student groups in CS and STEM fields, though much of the narrowing was associated with the introduction of the AP CS P exam. Our further investigations made it clear that overall participation rates on AP CS exams appear to be associated with CS policy adoptions, though none of these policies show any clear relationship with increasing the share of historically underrepresented groups among test-takers.

We recognize that some of these findings cut against a dominant narrative in CS education circles, which states that increased access to CS education will lead to narrowing participation gaps. While we do find gaps narrowing in recent years, these do not appear to be related to policy adoption. We clarify, however, that these results are based on a narrow dataset immediately in the wake of policy changes. These findings are not observed over long periods of implementation nor on a broad set of outcomes, which could counter these early patterns. For example, recall from our earlier discussion that white and Asian students are more likely to enroll in a richer set of STEM and AP-level courses generally , and they are more likely to engage in CS courses specifically . It seems probable that, as states kickstart CS education initiatives, the overrepresented student groups that enjoy preferred access may be better positioned to take advantage of newly available opportunities. Similarly, more fundamental outcomes like student exposure to coding or discussions of new technology in class (which contrast with the capstone AP CS outcomes in our data) may be more likely to have a disproportionate impact on underrepresented groups, narrowing formative exposure gaps. In either case, it seems plausible that narrowing CS and STEM participation gaps over a period of several years of policy implementation may still result even if AP CS gaps appear to be uncorrelated with short-term policy changes.

“Even as AP computer science test-taking has increased among underrepresented groups, the passing rate has also increased, resulting in narrower gaps with overrepresented students.”

Our results also provide some unambiguously encouraging news. First, even as AP CS test-taking has increased among underrepresented groups, the passing rate has also increased, resulting in narrower gaps with overrepresented students. Also, even states that have not been as active in promoting CS education policies have still shown large surges in AP CS participation; thus, even in the absence of policy action, we see reason to be optimistic about the trajectory of CS education overall.

We hope these findings invite reflection and re-evaluation of how states are approaching the expansion of CS education. As we close, we offer the following recommendations to state education agencies and policymakers working to expand CS education:

• Track multiple dimensions of CS education. CS is unique among academic disciplines in that it has previously been offered as an elective, but it is becoming more integrated into the academic core curriculum. Consequently, we do not have systematic measures in place tracking student competencies, access to coursework, teacher quality, or other similar outcomes as we do for core academic disciplines. More consistent measurement of inputs and outputs will help to steer states’ actions in CS.
• Prioritize diversity and inclusion in implementing CS policies. The oft-invoked link between expanding universal access to CS education and narrower participation and interest gaps in CS and STEM does have some empirical support, but certainly not enough to conclude that one necessarily leads to the other. Leaders and educators must ensure CS policies are implemented in inclusive ways to increase the chances of narrowing these persistent gaps. We encourage attention to both the classroom experiences of underrepresented student groups and CS educator diversity, too, as race- and gender-based role modeling are important predictors of future interest in CS and STEM .
• Take the long view on CS implementation. This report documents a flurry of activity around CS education in recent years, though we also urge patience and strategy here. Many states are still building the capacity to offer high-quality CS education—perhaps not so much in terms of physical capital (devices and broadband infrastructure), but more so in human capital (building capacity in the teacher workforce and scaling up high-quality instruction). By nature, these investments will take time to mature before students fully realize the benefits. We should not be discouraged by lackluster immediate results.

Computing and technology will be integral parts of the economic and social future awaiting the children of today. Providing access to high-quality CS education will be key in ensuring that all students can meet that future head on.

The authors thank Logan Booker and Marguerite Franco for excellent research assistance, and Nicol Turner Lee, Pat Yongpradit, and Jon Valant for helpful feedback.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Howmet Aerospace Foundation. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

• The nine policies that the State of CS annual report tracks were first described in a Code.org policy document, “ The Nine Policy Ideas to Make Computer Science Fundamental to K-12 Education ,” (n.d.), though we do not know of the policies being systematically tracked until the first State of CS report in 2017. The 2017 report included a 10th policy on promoting diversity in CS education, though this policy was dropped in subsequent years.
• The State of CS report counts states that have CS policies in progress (that is, the policy decision has been passed or issued, though the policies have a target implementation date in the future) as earning a half point on their policy tracker. Policies that have been passed and are implemented earn a full point. For ease of interpretation, we counted both implemented policies and policies in progress as earning a full point.
• We focus on Black, Latino, white, and Asian students because other racial/ethnic groups are inconsistently recorded over the time series; they represent roughly 90-95% of observations across years. Our results are qualitatively similar if we include other underrepresented racial/ethnic groups in the calculations.
• Student demographic information on 12th graders comes from the Department of Education’s Common Core of Data. Not all students taking an AP exam will be 12th graders, but we use their demographics as a baseline due to the tendency of younger cohorts of students to become progressively more racially diverse with time.
• This merging process results in three years in which we have observations of both CS education policies in place and AP CS outcomes (2018, 2019, and 2020). Because some of the policies documented in the 2017 State of CS report may not have been passed and implemented before the AP CS administration in the spring of that year, we lag all of the State of CS reports back one year before merging with AP CS exam results.

Education Policy K-12 Education

Governance Studies

Brown Center on Education Policy

Jing Liu, Cameron Conrad, David Blazar

May 1, 2024

Hannah C. Kistler, Shaun M. Dougherty

April 9, 2024

Darrell M. West, Joseph B. Keller

February 12, 2024

Amy J. Ko, Ph.D.

This is my computing education research faq..

I started it with the help of several computing education researchers at a Dagstuhl retreat in 2016. I consider it a community resource, so if you see something to add, fix, or improve, write me, or submit an issue or pull request .

Computing education research (CER), also known as computer science education (CSEd) research, is the study of how people learn and teach computing, broadly construed. This FAQ will teach you more about the field and how you might contribute to it.

What is computing education research? 🔗

First, CER is not teaching. Teaching is helping people change their knowledge, skills, attitudes, beliefs, identities. Research is discovery and invention. Teachers teach computing, whereas computing education researchers make discoveries about this teaching and learning, and invent new ways for these teaching and learning to occur. CER is an example of discipline-based education research , like math education research or science education research, all of which are part of the broader field of education and learning sciences research.

CER is also not educational technology research (EdTech). Computing education researchers often create educational technologies to support the learning and teaching of computing, but CER is not explicitly concerned with the broader use of technology in learning, teaching, and education. It's specifically concerned with the learning and teaching of computing in particular. Many computer science researchers invent learning technologies, but are not computing education researchers, because those technologies are not concerned with the learning of computing.

It's also important to note that I view "computing" broadly: it's not just about programming, or even just about computer science, but also about all of the phenomena surrounding computing, including data, information, privacy, security, ethics, software engineering, and sociocultural and sociopolitical views of computing in society. This means that computing education and CER can and does cover far more than just learning to code—it just hasn't historically.

What is the difference between 'CS' and 'computing'? 🔗

CS generally refers to the historically core topics in computer science research, such as theory, algorithms, data structures, programming languages, and operating systems. But other fields began to engage these ideas and identify their intersections to other fields. For example, information science, a field long concerned with data, information, and society, began to consider those topics from a computer science perspective. Science began to apply computer science ideas to data capture, storage, and analysis. Biology, as it began to see DNA as a form of biological data, and apply algorithms to analyzing it, formed bioinformatics. Communication began to explore computer-mediated communication. Behavioral and brain scientists started using computers to model decisions, knowledge, and brain activity. And social scientists of all kinds began studying and critiquing the role of computers in society.

This broadening of algorithms, data structures, and programming to all of academia put pressure on "computer science" as a phrase. Some universities with CS departments began to grow to teach and study all of these broader phenomena, leading many of them to rename themselves "Schools of Computing". The word "computing" was meant to refer not to physical hardware, but of computation itself and the many ways that it occurs and is organized in nature and human civilization. And thus, over time, CS began to connote the more narrow historical focus of CS foundations and Computing to the broader set of phenomena around the design and use of computers, including CS itself.

Given that history, "CS education" generally refers to the teaching and learning of these historically narrow topics in CS, and "CS education research" to research about this teaching and learning. In contrast, "Computing education" generally refers to the teaching and learning of any aspect of the use of computers and computational ideas, and "Computing education research" to research about that teaching and learning. Because I'm not interested in the historically narrow conception of computing, and my research considers topics outside that narrow historical conception, I use "Computing" instead of "CS".

How does computing education research compare to related fields? 🔗

My background isn't in these fields, though I do collaborate with people in these other communities and have learned about their differences. Here's the best characterization I can give:

• Education research is broadly concerned with formal systems of education, how to make those systems effective and just, how to prepare teachers to make them effective and just. The field is interested in general theories of learning, education, interest development, and identity, and because of its focus on formal education, is often focused on youth, who are the dominant age demographic engaged in formal education. The phrase "Computing education" uses the word "education" in this same way, but is more broadly concerned with teaching and learning in any context (in principle, but often not in practice).
• Educational psychology is focused on learning phenomena in the mind, such as learning, memory, development, intelligence, self-regulation, motivation, and self-concept. The field is also concerned with school psychologists who help students with their mental health. The field tends to be more quantitative than education research and learning sciences, following traditions of cognitive psychology. Computing education draws upon this field, especially in its history of cognitive theories of program understanding.
• Learning sciences emerged in the 1990's as a reaction to educational psychology's inattention to the setting, culture, and social context of learning. Combining perspectives from cognition, cognitive science, computer science, and design, like education research, it's much more concerned with the sociocultural factors that shape learning, and more than education and educational psychology, views design as a means to articulating theories, a way of shaping theories, and a way of testing theories. Because of the focus on context, in addition to being concerned with formal systems of education, it is also concerned with learning across the lifespan, at home, in families, and other settings.

(Shayan Doroudi provides a nice primer on learning theories and their disciplinary origins.)

How does computing education fit in to all of this? Like other discipline-based education research (DBER) such as math and physics education, it draws upon all three of the fields above, using theories and ideas from those fields. However, because it is focused on a discipline, it is specifically concerned with the content of the discipline, specific methods of learning and teaching that content. In this sense, it is more applied, bridging foundational ideas that span any human learning to applied ideas specific to the learning of specific ideas and skills.

What are major research questions in CER? 🔗

As with any research discipline, research questions can and should be specific. However, there are some major overarching questions in this field that researchers have begun to investigate, including:

• What is computing?
• What does it mean to know computing?
• How do people learn computing?
• How do teachers teach and assess computing?
• How does identity interact with people's learning of computing?
• How can people learn computing more effectively?
• How can teachers teach computing more effectively?
• How can access to computing education be improved?
• How can computing education be delivered equitably to all?
• How can computing education be reimagined to serve goals other than profit and disruption?
• How do systems of oppression such as racism, sexism, and ablism shape learning, teaching, and curricula?
• How can we implement anti-racist CS education?
• How can learning technologies teach computing?
• How does computing education affect people's lives?
• What are the societal costs of computing illiteracy?
• What can be taught about computing to learners of different ages?

While the "people" in the questions above could be anyone (youth, teens, college students, adults, and even teachers), the history of CER has primarily focused on teaching students in post-secondary settings, because the faculty conducting research have found it easier to study the students they are teaching. This is changing as countries around the world begin to incorporate computing into all levels of school, and as private industry begins to create technologies and services that teach computing to all ages. For example, my research has investigated new ways to teach youth from age 8-18, as well as adults.

What are some exciting CER discoveries? 🔗

There are so many! Examples include:

• The field discovered that diversity in computing education is low because of the narrow, exclusionary nature of computing cultures, not because of inherent disinterest or inability on the part of diverse learners (e.g., Fisher & Margolis 2002 , Margolis 2010 ).
• The field invented contextualized computing ed pedagogy (e.g., Mark Guzdial's media computation ), which has greatly increased the diversity of computer science graduates, and spread to many universities.
• The field built upon the earliest structured editors like the Cornell Program Synthesizer , eventually maturing them into block-based editing environments like Alice , Scratch and Blockly . These editors greatly increased engagement in computing education, and greatly reduced barriers to learning programming languages.
• Seymour Papert, who was broadly concerned with learning, but also the learning of computing, contributed constructionism, a new theory of learning ( Papert 1980 ).
• Alan Kay, one of the earliest researchers to investigate the learning of computing, helped build upon ideas of object-orientation from Simula , which inspired Smalltalk, which along with other languages such as C++, inspired the modern object-oriented programming languages and IDEs we use today.

The field's recent efforts to transform STEM education through computing, invent rapid new forms of learning online, and devise more equitable ways to teach should be equally, if not more impactful.

What jobs can computing education researchers get? 🔗

Most computing education researchers are faculty in universities. Many of these faculty are tenure-track faculty like myself, which means a substantial portion of our time (~50%) is spent on scholarship. However, there are also many full-time instructors who find additional time to do research on top of their teaching. Many of the original authors at ICER were once members of the Bootstrapping or Scaffolding groups (led by Fincher, Petre, and Tenenberg), who were CS teachers that started to do research in their own classrooms.

Not all computing education researchers are college faculty. Some work in industry creating educational technologies for teaching computing, applying their expertise to the research and design of educational software. Some work in non-profits, using their expertise to advocate for computing education in schools, while conducting research on factors that affect policy. Some work in school districts, helping to implement computing education curricula in schools, while studying and evaluating the effectiveness of the implementation. Others work in government, facilitating research funding. Others still become teachers themselves, both at universities and other schools.

Tenure-track faculty are in the best position to make advances in the field because a substantial portion of their time is dedicated to research, but the research contributions by teaching-track faculty are critical, as they often bring more richly informed perspectives on the practice of teaching. It is possible to do research in other positions, but it is often outside the scope of a job. Because of this, many non-tenure track faculty focus their research on settings that their job gives them access to, which can restrict which research questions they can answer.

How do I become a CER researcher? 🔗

The most effective route is to get a Ph.D. in computing education research at one of the many Ph.D. granting universities in the world. Ph.D. students learn to conduct research over the course of multiple years (generally 4 to 6) under the supervision of an advisor.

Undergraduate research is a key part of creating pathways to Ph.D. programs. Undergraduates can help accelerate research projects, and even lead their own projects, helping with admission to Ph.D. programs (especially if you publish, which demonstrates your interest and ability in conducting research). See the CRA-E best practices guide on undergraduate CS research for a glimpse into how effective undergraduate research experiences should work.

Where can I get a Ph.D. in CER? 🔗

You need to find a university that grants Ph.D.s and has tenure-track faculty who do research in CER on a topic that you're interested in. The alphabetical list below contains some of the many faculty who advise Ph.D. students on computing education research. Find them online and see what kind of research they're doing. (This list may be out of date, as faculty sometimes move universities, retire, go to industry, or change research areas, so be sure to check their website for the latest information).

One common question is whether to get a Ph.D. in a CS department, a College of Education, or some other kind of computing or learning related department, such as information schools, which are often concerned with computing and data literacy. Ultimately, the doctoral program you choose is going to shape a few things: 1) the classes you're required to take in the first year or two, 2) the peers you might sit near and talk to, 3) the faculty who might serve on your committee and what expertise and values they have, and 4) what resources you have to get particular kinds of jobs. For example, if you go to a CS Ph.D. program, you're going to learn about the latest research in various areas of CS, be surrounded by people interested in computing, but possibly not many interested in computing education; you'll have resources for getting faculty jobs in CS departments, but not really Colleges of Education. In contrast, if you go to a College or School of Education Ph.D. program, you're going to learn about the latest knowledge in education and learning sciences, and be surrounded by people passionate about learning, equity, and justice, but possibly not many people interested in computing. And if you go to a place like an information school, you'll gain new perspectives about data and computing, be surrounded by a radical diversity of people with interests that span many disciplines, but possibly one of only a few people interested in computing education.

Because of the tradeoffs above, the best places to go are ones where there are advisors that share your interests, a critical mass of people interested in computing education, and healthy interchange between academic units interested in computing, learning, and data. This is because computing education is inherently interdisciplinary; you want peers and faculty that appreciate that, value that, and support that.

One note about selecting advisors: their disciplinary affiliation is just one indicator of the nature of the contributions they might make (people in CS departments might built learning technologies, people in colleges of education might focus on teacher training and pedagogy), but this is not a perfect indicator. Look closely at researchers' recent publications; and if their websites seem out of date, write them to ask what they're working on.

Another caveat: some of the faculty below have chosen their expertise descriptions, but others I had to extract from faculty websites wrote. I've put a * next to expertise that hasn't been chosen or agreed to by the researcher being described. These expertise tags are also likely to be perpetualy out of date, as researchers pursue new topics. The best thing to do is click on their name to visit their website and see what kinds of research they have published. That's the most direct indicator of their interests, the methods they use, and the types of contributions they want to make (other than just writing them and asking, which you can also do).

For doctoral admissions, how important is it to focus on a single research area? 🔗

Advisors differ on the criteria they use to select candidates. Personally, I look for 1) experience with research, 2) passion in the subject of computing education, 3) the requisite skills to pursue that passion, and 4) an overlap with my interests. You can get experience by working with faculty at your own institution. That can be hard if you don't have faculty doing work in this area. The requisite skills depend a lot on the contributions you want to make. If you want to envision and build new learning technologies, can you code well enough to build them? If you want to investigate new teacher training methods, do you have teaching experience? If you want to do more theoretical work, how strong are your writing and analytical skills? All of these skills end up being important in some way to participating in CER discourse, just to varying degrees.

Working specifically in computing education isn't necessary to achieve the above. Perhaps you have undergraduate research experience in HCI, software engineering, or programming languages. That can be fine, as long as your passion is clear and the skills you have align with the questions you want to answer. Researchers are always investigating new questions, so it's perfectly normal to have experience from other related areas of computing and information science.

Of course, even if you meet all of the criteria above (or other criteria that other advisors might have), you might not get accepted. That's because doctoral advising is extremely time-intensive: we commit to advise people for anywhere from 3-6 years or more, and so we can only take on so many students at a time. There might be a dozen people who apply to work with one of us, but we only have capacity to admit one or two at most.

Where can I find a CER job? 🔗

There are many places where global CS education-related jobs are posted:

• The #jobpostings channel on the CSforAll Slack :
• The CRA Jobs website
• The SIGCSE-jobs mailing list
• The Higher Ed Jobs
• Evan Peck maintains a list of CS faculty postings from "PUIs" (primarily undergraduate institutions).
• Many organizations, including non-universities, hire postdocs (e.g., Quinn Burke at Digital Promise , has hired postdocs and consultants)

Monitor those closely for opportunities. The field is growing, but in unconventional ways: there are tenure-track positions, teaching-track positions, professor of practice positions, postdocs, research and development positions in not-for-profits, and much, much more.

Is there funding for CER? 🔗

Yes! At least in the U.S., Ph.D. students are generally funded by the research grants their advisors obtain, and can also receive NSF Graduate Research Fellowships, which cover three years of tuition and stipend. Undergraduates can participate in NSF-sponsored Research Experience for Undergraduate projects that faculty sponsor. CER faculty can also apply for NSF CAREER grants on computing education research, or an NSF Research Initiation Initiative for new faculty. Most Ph.D. granting institutions also offer teaching assistantships. In the United States, there are also regularly programs that fund CER. This changes frequently, but here is a current snapshot as of 2016:

• NSF CS for All . Funds basic research on CS education as well as researcher-practitioner partnerships focused on building K-12 CS education capacity, access, participation, and engagement.
• NSF IUSE . Funds programs that improve the quality of and access to STEM education in undergraduate programs. Does not directly fund basic research.
• NSF DUE . Funds innovations in STEM education at 2- and 4-year colleges.
• NSF ITEST . Funds programs that broaden participation in STEM. Does not directly fund basic research.
• NSF DRK-12 . Funds projects that enhance the quality of and access to STEM education in K-12, including basic research.
• NSF RETTL . Funds projects on Emerging Technologies for Teaching and Learning, including intelligent tutors, computer-based instruction, computational tools for learning, etc.
• NSF EHR CORE Research . Funds basic education research. Not CS specific, but it has separate tracks within its reviewing structure for CS and engineering.

What do I need to know to be an effective researcher? 🔗

First, you need to know some computing yourself. That doesn't mean you need an entire computer science degree, but it helps to have learned to code a bit, and to understand what an algorithm and a data structure is. It can also help to understand the culture of computer science as an academic discipline. Taking the first few introductory courses in a CS department is usually enough to provide this content knowledge foundation, unless you want to do research on the teaching of more advanced topics in CS.

Another thing to know is what makes good computing education research. One guide is to read peer review criteria. For example, ACM TOCE maintains and evolves a list of nuanced and pluralist peer review criteria that cover many kinds of research.

Beyond that, there is a substantial prior work to learn before you can make original discoveries. I've organized some of the major works into categories below, to focus your reading.

Education research foundations

As computing education research is a discipline-based kind of education research, foundations in education research are key. Below are essential works for conducting research on learning and teaching:

• How People Learn: Brain, Mind, Experience, and School and How People Learn II: Learners, Contexts, and Cultures provide an essential foundation in the major discoveries and theories of learning sciences and education research. Anyone doing research on learning should know everything in these books.
• Power and Privilege in the Learning Sciences: Critical and Socialcultural Theories of Learning presents foundational theories of learning that powerfully shape who learns.
• Research Methods for Social Justice and Equity in Education presents key methods for conducting education research in equitable ways, but also about equity and justice.
• There are also several notable commentaries on tensions between qualitative and quantitative data, including Hammer and Berland's arguments about qualitative coding as generating claims , McDonald et al's guidelines for inter-rater reliability practices , Soden, et al's guide for how to evaluate interpretive research , and Chi's classic guide for quantifying verbal data .

Race and Technology

While not specifically about computing education, these books critically examine the role of computing in justice. The ideas in these books are key to understanding the social implications of computing on society. I focus on race in particular because race, at least in the United States, has structured injustice more heavily than all other social categories, making it critical to understanding the effects of computing.

• Race After Technology examines how racism is embedded in software and the role of computer scientists and the software industry in reinforcing racism.
• Black Software: The Internet and Racial Justice, from the AfroNet to Black Lives Matter explores the long history of racial justice movements organized online and how the impact of their innovations have been erased with false narratives social media company innovation.

CER literature reviews

These works summarize bodies of knowledge in computing education research, helping you to more quickly learn what the field has discovered. All of these are essential reading.

• The Cambridge Handbook of Computing Education Research is a carefully edited synthesis of all of the major discoveries in computing education research since its beginning as a field 50 years ago up until 2018. I authored several chapters along with more than a dozen other leading researchers with the goal of creating the definitive introduction to the field. It is reflective of prior work, for better or worse, going deeply into pedagogy, but only briefly (but elegantly) addressing issues of racism, sexism, and inclusion. Therefore, it should not be viewed as a vision for the future of the field, but rather a reflection of its past.
• Learner-Centered Design of Computing Education: Research on Computing for Everyone is a wonderful synthesis of computing education research, with a focus on pedagogy for anyone learning computing, rather than just computer science students.
• Computational Thinking in K-12 A Review of the State of the Field examines the state of discourse about "Computational Thinking", a contested idea that has spread broadly throughout K-12 CS education, despite its questionable soundness as an idea.
• Introductory programming: a systematic literature review . The result of an ITiCSE working group, analyzes the literature along many facets: students, teachings, the curriculum, and assessment, and surfacing directions for future research.
• A survey of literature on the teaching of introductory programming summarizes papers on classroom instruction in introductory programming courses in higher education.
• Constructing a core literature for computing education research presents an appendix of impactful papers published before 2005.
• Misconceptions in programming is a great review of the broad literature on misconceptions that people form about programming languages.
• Lowering the Barriers to Programming: A Taxonomy of Programming Environments and Languages for Novice Programmers surveys hundreds of programming languages and environments intended to support learning to code. There have been many more since its publishing in 2005, but before ever inventing one of your own, it's important to know what's been invented already.
• The State of the Art in End-User Software Engineering summarizes the literature on end-user programming, which is related, but not the same as novice programming. It synthesizes of all of the programming languages, environments, and tools that have helped people learn to code while automating a task.

Notable works

Everyone working in CER should have read these books and understand their implications for research and practice.

'Underrepresented Minority' Considered Harmful, Racist Language is a short blog post that discusses the terminology we use when we discuss diversity in computer science.

Mindstorms: Children, Computers, and Powerful Ideas is a classic book that envisions a theory of learning grounded in the construction of knowledge through personally meaningful tinkering and creation, especially with computers. I summarized the book in a blog post.

Stuck in the Shallow End: Education, Race, and Computing illustrates the numerous racist structures, beliefs, and practices in K-12 education that systematically exclude students of color from CS education.

Unlocking the Clubhouse: Women in Computing examines how the culture of higher education CS systematically excludes and deters women from participating in CS education, and explores promising practices for changing this culture.

Epistemological Pluralism: Styles and Voices Within the Computer Culture presents a critique of academic computing culture for is exclusion of diverse interests and ways of knowing.

When Twice as Good Isn't Enough: The Case for Cultural Competence in Computing critiques CS departments for being uncritical of themselves, their curricula, and the software industry, advocating for cultural competence amongst faculty and students.

The Intersection of Gender, Race and Cultural Boundaries, or Why is Computer Science in Malaysia Dominated by Women? examines the inherent intersectional complexity of race, gender, and culture that shapes participation in computing education.

They can't find us: the search for informal CS education demonstrates how search engines, CS education terminology, and culture interact to connect educated White families to informal CS learning opportunities, while obscuring them from less privileged families.

Visions of Computer Science Education: Unpacking Arguments for and Projected Impacts of CS4All Initiatives analyzes the abundance of arguments for K-12 CS for All efforts, and how they intersect with varying political ideologies.

On Theory Use in Computing Education Research examines the use of theory in computing education and how it is often weaponized to prevent the publication of new ideas.

Ethics, Identity, and Political Vision: Toward a Justice-Centered Approach to Equity in Computer Science Education advocates for CS education researchers and teachers to more directly engage the sociopolitical context of CS education curricula and teaching.

Halving fail rates using peer instruction: a study of four computer science courses presents one of the few rigorously examined teaching methods that promotes improved learning, especially for students marginalized by CS education cultures.

African American men constructing computing identity examines how race, culture, and stigma can warp genuine interests in computing, and how informal learning interventions can counter these forces.

COMPUGIRLS’ Standpoint: Culturally Responsive Computing and Its Effect on Girls of Color illustrates the impact of culturally repsonsive computing on girls of color.

Digital Youth Divas: Exploring Narrative-Driven Curriculum to Spark Middle School Girls’ Interest in Computational Activities explores how to engage girls of color by centering their stories.

Becoming Technosocial Change Agents: Intersectionality and Culturally Responsive Pedagogies as Vital Resources for Increasing Girls’ Participation in Computing explores the importanc of intersectional views on culturally responsive pedagogy.

If you've read all of the above and are looking for more literature, be sure to follow all of the SIGCSE conferences, and other relevant education and learning science journals, monitoring the ACM Digital Library and the NSF funded website CSEdResearch.org , which surveys the broad expanse of CS education research, including article summaries and evaluation instruments.

What books provide guidance on CS teaching? 🔗

While there are many books that provide guidance on teaching in general (e.g., Tools for teaching (Davis, 2009), How learning works: Seven research-based principles for smart teaching (Ambrose et al., 2010), Teaching what you don’t know (Huston, 2009), What Works Clearinghouse ), there are only a handful of books written to guide CS educators (alphabetically):

• The Big Book of Computing Pedagogy offers a collection of CS teaching practices and methods written by CS educators and CS education researchers.
• Coding as a playground: Programming and computational thinking in the early childhood classroom (Bers, 2017). A review of the opportunities in teaching younger children to code.
• Computational Thinking (Denning & Tedre, 2019). A historical introduction to computational thinking.
• Computational thinking and coding for every student (Krauss and Prottsman, 2016). Includes strategies and activities for teaching computational thinking, with several lessons and annotated resources.
• Computer Science K-12: Imagining the Possibilities! (Bergman, 2018). Full of rich case studies, activities, projects, and practical guidance on organizing and managing CS classrooms.
• Computer Science in K-12: An A-Z Handbook on Teaching Programming (Grover, 2020) includes 26 chapters featuring foundational programming concepts and practices, as well as well-researched pedagogies for teaching introductory programming. With chapter contributions from researchers and classroom teachers, the book shares concrete examples as well as abstract principles distilled from research studies and classroom practice along with many illustrative examples (in block- and text-based programming) that can be used in classrooms.
• Computer Science Teacher: Insight Into the Computing Classroom (Clark, 2017). Focuses on secondary CS teaching and what the role entails, providing a rich set of case studies and quotes.
• Computational Thinking in Education: A Pedagogical Perspective (Yadav and Berthelsen, 2022), explores the relevance of computational thinking in primary and secondary education, giving an overview of what computational thinking is and how to integrate it into learning, instruction, and assessment.
• Computer Science Education: Perspectives on teaching and learning in school (Sentance et al. 2018). An edited book full of rich summaries about CS education research, but written less for teachers and more for those interested in research.
• Connected Code: Why Children Need to Learn Programming (Kafai et al., 2016). Argues for moving beyond computational thinking to computational participation, leveraging social networks and digital making. Discusses examples of youth participation with programmable toys, tools, and textiles and the ethical challenges that emerge in these social contexts.
• Critically Conscious Computing: Methods for Secondary Education (Ko et al., 2021). A critical survey of CS and CS education foundations and a collection of dialogic teaching methods for promoting student critical consciousnes about computing and society.
• Guide to teaching computer science: An activity-based approach (Hazzan et al. 2015). Includes detailed learning activities, curriculum reviews, CS education research, lesson planning, and course design. Some pre-service CS teachers find the concrete examples help; others find it jargony and overly complex.
• Invent to learn: Making, tinkering, and engineering in the classroom (Martinez and Stager, 2013). A practical guide to bringing tinkering into the classroom via project-based learning.
• Preparing Pre-Service Teachers to Teach Computer Science: Models, Pracitces, and Policies (Mouza, Yadav, Ottenbreit-Leftwich, 2021). An edited volume surveying research on CS teacher education, offering examples for how to design and deliver effective teacher preparation.
• Teaching Computing in Secondary Schools (Lau, 2017). Offers a framework for planning and delivering CS curricula.
• Teaching Computing: A Practitioner's Perspective (Walker, 2018). Full of teaching tips for higher education faculty teaching CS.
• Teaching tech together . This is an informal survey of research useful for teaching programming. Greg put it together to help others become better teachers of computing.
• Your First year teaching computer science (Gregg, 2021).
• Code in Every Class (Brookhouser and Megnin, 2017).
• Integrting Computer Science Across the Core: Strategies for K-12 Districts (Lynch and Ardito, 2020).
• Creative Coding: Lessons and Strategies to Integrate Computer Science Across the 6-8 Curriculum (Caldwell, 2018)

Is one missing from this list? Let me know and I'll add it.

What conferences and journals publish CER? 🔗

Most academic fields have exclusively academic venues for publication, with few practitioners participating in or reading the research that researchers produce. The CER community is unique (and I believe quite fortunate) in that practitioners are deeply involved in the academic research community (partly because most faculty conducting research are teachers themselves). Below I note several conferences and journals where you can publish computing education research (see SIGCSE for a broader list ). Note that I separate the pure research venues from the venues that combine both research and practice since the combined venues are often dominated by practioners, which can make it hard to have focused research conversations and rigorous peer review.

Research only venues

ICER (the ACM International Computing Education Research conference) is the only academic conference that strictly publishes research. All of the reviewers who peer review submissions are trained researchers with Ph.D.s. ICER tends to focus on theoretically, methodologically, and empirically-rich work, advancing the science of computing education. It is held around the world but is generally in North America every other year.

TOCE (the ACM Transactions on Computing Education) publishes research, and is similar in scope to ICER, but in a journal format. Like ICER, the editorial board and reviewers are all trained researchers.

CSE (the Journal of Computer Science Education) publishes research and is similar to TOCE and ICER in its reviewing community and similar in research rigor and prestige. However, unlike TOCE and ICER, publications in CSE are generally expected to have more direct implications for teachers.

ICLS (the International Conference on Learning Sciences) does not strictly focus on computing education, but publishes high quality research on learning sciences. Accepts both qualitative and quantitative work, especially of mixed methods. Also tends to focus more on K-12 than the venues focusing strictly on CER.

JLS (the Journal of Learning Sciences) is one of the top education research journals and expects a strong connection to learning theory and mostly wants empirical work. It is not a journal that publishes HCI, so work must be connected to cognition, sociocultural context, or other theory, and not system design.

CSCL (the International Conference on Computer-Supported Collaborative Learning) focuses on issues related to learning through collaboration and promoting productive collaborative discourse with the help of the computer and other communications technologies.

IJCSCL (the International Journal of Computer-Supported Collaborative Learning), like CSCL, focuses on learning through collaboration.

L@S (the ACM Conference on Learning at Scale) is a computer science conference that focuses on techniques for scaling instruction. Some of the work published here concerns computing education, but many other domains are represented as well. Often focuses on MOOCs and other forms of online learning.

RESPECT (the IEEE Conference on Research on Equity and Sustained Participation in Engineering, Computing, and Technology) is a conference focused on engagement, participation, and equity in STEM fields. It has research and experience report tracks, and expects empirical papers grounded in theory.

IDC (ACM SIGCHI Interaction Design and Children) is an HCI conference with a focus on children, focusing on design artifacts for kids and enabling kids to be designers, with a special focus on participatory design as a methodology.

CHI (ACM SIGCHI Conference on Human Factors in Computing) is an HCI conference with a focus on any aspect of interactions between people and computers, including programming. As one of the largest and broadest ACM conferences, it's easy for research on learning to get lost here, but so does every other topic!

AERA (the American Education Research Association conference) has a division for engineering and computing education that publishes papers on computational thinking.

JEE (the Journal of Engineering Education). High-quality but with few international collaborations (like the MIMN studies in CER). Occasionally has papers related to computing.

IEEE Transactions on Education . I know little about this journal. Feel free to share opinions!

EDM (the International Conference on Educational Data Mining). Explores using educational data to understand student learning.

JEDM (the Journal of Educational Data Mining). Publishes research on the use of data mining in education.

Research and practice venues

• SIGCSE (the SIGCSE Technical Symposium on Computer Science Education) publishes both research and practice papers in a short format, bringing together researchers and teachers. This is the largest conference on computer science education and generally attracts teachers. There is a dedicated research track separate from experience reports, though the research track has a 6-page limit, making it unsuitable for many forms of research, such as qualitative work or more substantial quantitative work. Generally held in North America.
• ITiCSE (the Annual Conference on Innovation and Technology in Computer Science Education) publishes both research and practice papers, with a focus on practice. Generally held in Europe.
• Koli Calling (International Conference on Computing Education Research), held in Finland every year, publishes research and practice papers with a focus on qualitative research. A small but dedicated community.
• WiPSCE (Workshop in Primary and Secondary Computing Education) aims to bring together researchers and practitioners, and publishes both research and practice papers. It is generally held in Europe.
• ACE (the Australasian Computing Education Conference) is a regional conference with a mix of research and practice papers, bringing together education researchers and practitioners. Held in Australia or New Zealand, but welcomes attendees from anywhere.
• LaTiCE (the International Conference on Learning and Teaching in Computing and Engineering) publishes both research and practice papers. Held primarily in Asia.
• FIE (the ASEE Frontiers in Education conference) is more broad and more practitioner focused than SIGCSE and occasionally has CER work.

What is SIGCSE? 🔗

SIGCSE , like other ACM Special Interest Groups (SIGs), is an organization that focuses on a particular topic within ACM, namely computer science education. It sponsors ACM conferences (e.g., the SIGCSE Technical Symposium and ICER) and influences their structure and focus. Note that SIGCSE the group organizes SIGCSE the conference. I know, it's confusing, but aren't you glad you read this?

What's the difference between a research paper and an experience report? 🔗

This is an important question, since many of the conference venues in the computing education community publish both. Unfortunately, the community hasn't developed much clarity about the differences between these. The result is that many papers published in the SIGCSE experience report track look like research papers, and many of the papers published in the SIGCSE research track look like experience reports. What's the essential difference?

In my opinion, the key distinction between research and an experience report is your audience, which implies your goals: are you writing to researchers, who aspire to build upon everything we know to advance theories about what we know about CS teaching and learning? In contrast, if you're writing to teachers, you're likely sharing practical knowledge, such as an interesting method you tried, a surprising experience, or a teaching method others might experiment with. The critical difference is that in research, we're trying to be certain that we know something, but it's okay if we don't know how to put that knowledge into action yet, whereas in practice, we're trying to learn how to teach something, even if we're not certain it will work. Another way to characterize the difference are some of the evaluation criteria. Research papers should be novel with respect to everything we know and sound , but not necessarily immediately useful. Experience report papers should be novel with respect to common knowledge (but not necessarily novel with respect to all knowledge), useful and interesting , but not necessarily sound.

I believe that both are valuable in their own ways. Research allows us to build confidence in what we know, whereas sharing experience allows us to teach each other. We need both for a thriving practice of CS teaching and a thriving body of knowledge to inform that practice.

How can I keep up with the latest research, practice, and policy? 🔗

There are a few excellent blogs (in alphabetical order):

• Mark Guzdial's Computing Education Research blog has been active since 2009 and contains thousands of posts that explain computing education research to a broader community.
• Felienne Hermans has a blog about programming and inclusion .
• Amy Ko's Bits & Behavior publication at Medium covers CER, software engineering, HCI, and broader issues in academia.
• Shriram Krishnamurthi's Parenthetically Speaking discussions a range of topics on academia, programming languages, and computing education.
• William Lau's blog on CS education and teaching more broadly .
• Lauren Margulieux's blog discusses learning sciences, discipline-based education reseach, and computing education.
• Alfred Thompson's Computer Science Teacher blog covers a range of computing education, research, and policy issues.

Is this list missing you? Let me know!

How can I connect with the community? 🔗

Post-pandemic, many online communities have frayed. Here are a few that remain:

• The CS for All Consortium maintains a Slack team . Join as a member to access it.
• There's a private group on Facebook called Computer Science Education: Researchers and Practitioners .

• Search SF State Search SF State Button SF State This Site

Computer Science Education Research

Our computer science education program prioritizes inclusivity and diversity aiming to provide equitable access to computer science learning for students from various backgrounds. It offers a comprehensive curriculum, teacher training, and resources that cater to diverse populations, emphasizing the importance of digital literacy and computational thinking. The program encourages underrepresented groups to engage in coding, problem-solving, and technology exploration, fostering a more inclusive and empowered generation of young learners in the field of computer science.

Academics and Coursework

Our Computer Science education research program has developed exceptional certificate programs and continuing education programs aimed at enhancing CS skills and literacy, preparing a diverse workforce for the evolving field of computing. In addition to broader requirements for a CS undergraduate or graduate degree, students may specialize their education around Computer Science Education research topics via these undergraduate and graduate level courses, minors, and certificate programs:

Undergraduate Courses

K-12 CS Teacher Supplementary Authorization Program

Computing Application Minor

Data Science and Machine Learning for Biotechnology Certificate

Faculty and Focus Areas

Publications and reports.

Ihorn, S., Kulkarni, A., & Yoon, I. (2023). Sustaining and expanding student training and support efforts beyond the NSF support period. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.

Gautam, A., Ihorn, S., Yoon, I., & Kulkarni, A. Foundational strategies to support students with diverse backgrounds and interests in early programming. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD.

Gautam, A., Kim, M.,  Ihorn, S., Yoon, I., & Kulkarni, A. (2023) Foundational strategies to support students with diverse backgrounds and interests in early programming. In ASEE: American Society for Engineering Education, July 25-28, 2023, Baltimore, MD. 

Zimmerman, T., Esquerra, R., Chan, Y.H.M., Kulkarni, A., Adelstein, N., Albright, A., Luo, J., Dean, Z., Ahmed, S., Phillips, M. and Bianco, S., (2023). Teaching Image Processing and Optical Engineering to University Biology Students. The Biophysicist, 4(1), pp.38-57. https://doi.org/10.35459/tbp.2022.000240

Reyes, R.J., Hosmane, N., Ihorn, S., Johnson, M., Kulkarni, A., Nelson, J., Savvides, M., Ta, D., Yoon, I. and Pennings, P.S., (2022). Ten simple rules for designing and running a computing minor for bio/chem students. PLOS Computational Biology, 18(7), p.e1010202. https://doi.org/10.1371/journal.pcbi.1010202

Kulkarni, A., Ihorn, S., Tate, C., Nelson, J., Hosmane, N., Adelstein, N., Pennings, P., Jacques, T. and Yoon, I., (2021). January. Peer Mentoring in an Interdisciplinary Computer Science Training Program: Mentor & Student Perspectives and Lessons Learned. In Zone 1 Conference of the American Society for Engineering Education.

Ihorn, S., Yoon, I. and Kulkarni, A., 2020, February. Student psychological factors and diversity in computer science education. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 1380-1380).

Grants and Awards

National Science Foundation. CS4NorthCal: Scaling an Evidence-based Model for Teacher Preparation and Support to Provide Equitable and Inclusive CS Education in California High Schools. 10/01/2022-09/31/2026. (PI: Yue)

National Science Foundation. BPC-A: Socially Responsible Computing: Promoting Latinx student retention via community engagement in early CS courses. 10/1/2022-9/30/2025. (PI: Yoon)

National Science Foundation. HSI Implementation and Evaluation Project: Self-sustaining Peer Mentor Support System for Computer Science Students. 08/01/2022-07/31/2025. (PI: Wang)

Center of Inclusive Computing, Northeastern ( https://cic.northeastern.edu/ ). Redesign Introductory CS Courses. 6/1/2022-5/31/2024. (PI: Yoon) 

California State University Chancellor’s Office. Math and Science Teacher Initiative (MSTI) Supplement Grant. 09/01/2020-08/31/2023. (PI: Yue)

Genentech Inc. Data Science, Artificial Intelligence and Biotechnology Certificate program. 8/1/2020-7/31/2023. (PI: Kulkarni)

Genentech Foundation. Gene-PINC Scholarship. 8/1/2020-7/31/2023. (PI: Kulkarni) 

National Science Foundation. Collaborative Research: CS4SF: A Scalable Model for Preparing High School Teachers to Provide Rigorous, Inclusive Computer Science Instruction. 10/01/2018-09/31/2021. (PI: Yue)

National Science Foundation. Building the Diverse, Multidisciplinary Computer Science Workforce of the Future with Promoting Inclusivity in Computing (PINC) 2.0. 10/2018-9/2023 (PI: Yoon).

National Science Foundation. Scholarships To Improve Undergraduate Students' Academic Achievement, Retention, and Career Success in Computer Science and Artificial Intelligence. 8/7/2020-2/28/2025. (PI: Kulkarni)

• Email: [email protected]
• Telephone: (415) 338-1244

Office Hours

Quick links.

• Future Students
• News @ SF State

• News Releases

Top ten computer science education research papers recognized

SIGCSE Symposium highlights research that has shaped the field

Association for Computing Machinery

As a capstone to its 50th annual SIGCSE Technical Symposium, leaders of the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) are celebrating the ideas that have shaped the field by recognizing a select group of publications with a "Top Ten Symposium Papers of All Time Award." The top ten papers were chosen from among the best papers that were presented at the SIGCSE Technical Symposium over the last 49 years.

As part of the Top Ten announcement today in Minneapolis, the coauthors of each top paper will receive a plaque, free conference registration for one co-author to accept the award and up to a total of $2,000 that can be used toward travel for all authors of the top ranked paper.

"In 1969, the year of our first SIGCSE symposium, computing education was a niche specialty" explains SIGCSE Board Chair Amber Settle of DePaul University, of Chicago, USA. "Today, it is an essential skill students need to prepare for the workforce. Computing has become one of the most popular majors in higher education, and more and more students are being introduced to computing in K-12 settings. The Top Ten Symposium Papers of All Time Award will emphasize the outstanding research that underpins and informs how students of all ages learn computing. We also believe that highlighting excellent research will inspire others to enter the computing education field and make their own contributions."

The Top Ten Symposium Papers are:

Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a concept inventory for Computing Fundamentals: investigation of student misconceptions in a series of core CS1 topics previously identified as both important and difficult. Formal interviews with students revealed four distinct themes, each containing many interesting misconceptions.

Pair programming is a practice in which two programmers work collaboratively at one computer, on the same design, algorithm, or code. Prior research indicates that pair programmers produce higher quality code in essentially half the time taken by solo programmers. The authors organized an experiment to assess the efficacy of pair programming in an introductory Computer Science course. Their results indicate that pair programming creates a laboratory environment conducive to more advanced, active learning than traditional labs; students and lab instructors report labs to be more productive and less frustrating.

During a year-long study, the authors examined the experiences of undergraduate women studying computer science at Carnegie Mellon University, with a specific eye toward understanding the influences and processes whereby they attach themselves to or detach themselves from the field. This report, midway through the two-year project, recaps the goals and methods of the study, reports on their progress and preliminary conclusions, and sketches their plans for the final year and the future beyond this particular project.

Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. This paper adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, the authors provide evidence that introductory computing instructors can successfully implement PI in their classrooms.

Schneider describes the crucial goals of any introductory programming course while leaving to the reader the design of a specific course to meet these goals. This paper presents ten essential objectives of an initial programming course in Computer Science, regardless of who is teaching or where it is being taught. Schneider attempts to provide an in-depth, philosophical framework for the course called CSI -- Computer Programming I -- as described by the ACM Curriculum Committee on Computer Science.

Constructivism is a theory of learning which claims that students construct knowledge rather than merely receive and store knowledge transmitted by the teacher. Constructivism has been extremely influential in science and mathematics education, but not in computer science education (CSE). This paper surveys constructivism in the context of CSE, and shows how the theory can supply a theoretical basis for debating issues and evaluating proposals.

Introductory computer science students have relied on a trial and error approach to fixing errors and debugging for too long. Moving to a reflection in action strategy can help students become more successful. Traditional programming assignments are usually assessed in a way that ignores the skills needed for reflection in action, but software testing promotes the hypothesis-forming and experimental validation that are central to this mode of learning. By changing the way assignments are assessed--where students are responsible for demonstrating correctness through testing, and then assessed on how well they achieve this goal--it is possible to reinforce desired skills. Automated feedback can also play a valuable role in encouraging students while also showing them where they can improve.

Gries argues that an introductory course (and its successor) in programming should be concerned with three aspects of programming: 1. How to solve problems, 2. How to describe an algorithmic solution to a problem, and 3. How to verify that an algorithm is correct. In this paper he discusses mainly the first two aspects. He notes that the third is just as important, but if the first two are carried out in a systematic fashion, the third is much easier than commonly supposed.

This study was conducted to determine factors that promote success in an introductory college computer science course. The model included twelve possible predictive factors including math background, attribution for success/failure (luck, effort, difficulty of task, and ability), domain specific self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender. Subjects included 105 students enrolled in a CS1 introductory computer science course at a midwestern university. The study revealed three predictive factors in the following order of importance: comfort level, math, and attribution to luck for success/failure.

An objects-first strategy for teaching introductory computer science courses is receiving increased attention from CS educators. In this paper, the authors discuss the challenge of the objects-first strategy and present a new approach that attempts to meet this challenge. The approach is centered on the visualization of objects and their behaviors using a 3D animation environment. Statistical data as well as informal observations are summarized to show evidence of student performance as a result of this approach. A comparison is made of the pedagogical aspects of this new approach with that of other relevant work.

Annual Best Paper Award Announced

Today SIGCSE officers also announced the inauguration of an annual SIGCSE Test of Time Award. The first award will be presented at the 2020 SIGCSE Symposium and recognize research publications that have had wide-ranging impact on the field.

The Special Interest Group on Computer Science Education of the Association for Computing Machinery (ACM SIGCSE) is a community of approximately 2,600 people who, in addition to their specialization within computing, have a strong interest in quality computing education. SIGCSE provides a forum for educators to discuss the problems concerned with the development, implementation, and/or evaluation of computing programs, curricula, and courses, as well as syllabi, laboratories, and other elements of teaching and pedagogy.

ACM, the Association for Computing Machinery, is the world's largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

• Privacy Policy

Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

• Using machine learning to detect and prevent cyber attacks
• Developing algorithms for optimized resource allocation in cloud computing
• Investigating the use of blockchain technology for secure and decentralized data storage
• Developing intelligent chatbots for customer service
• Investigating the effectiveness of deep learning for natural language processing
• Developing algorithms for detecting and removing fake news from social media
• Investigating the impact of social media on mental health
• Developing algorithms for efficient image and video compression
• Investigating the use of big data analytics for predictive maintenance in manufacturing
• Developing algorithms for identifying and mitigating bias in machine learning models
• Investigating the ethical implications of autonomous vehicles
• Developing algorithms for detecting and preventing cyberbullying
• Investigating the use of machine learning for personalized medicine
• Developing algorithms for efficient and accurate speech recognition
• Investigating the impact of social media on political polarization
• Developing algorithms for sentiment analysis in social media data
• Investigating the use of virtual reality in education
• Developing algorithms for efficient data encryption and decryption
• Investigating the impact of technology on workplace productivity
• Developing algorithms for detecting and mitigating deepfakes
• Investigating the use of artificial intelligence in financial trading
• Developing algorithms for efficient database management
• Investigating the effectiveness of online learning platforms
• Developing algorithms for efficient and accurate facial recognition
• Investigating the use of machine learning for predicting weather patterns
• Developing algorithms for efficient and secure data transfer
• Investigating the impact of technology on social skills and communication
• Developing algorithms for efficient and accurate object recognition
• Investigating the use of machine learning for fraud detection in finance
• Developing algorithms for efficient and secure authentication systems
• Investigating the impact of technology on privacy and surveillance
• Developing algorithms for efficient and accurate handwriting recognition
• Investigating the use of machine learning for predicting stock prices
• Developing algorithms for efficient and secure biometric identification
• Investigating the impact of technology on mental health and well-being
• Developing algorithms for efficient and accurate language translation
• Investigating the use of machine learning for personalized advertising
• Developing algorithms for efficient and secure payment systems
• Investigating the impact of technology on the job market and automation
• Developing algorithms for efficient and accurate object tracking
• Investigating the use of machine learning for predicting disease outbreaks
• Developing algorithms for efficient and secure access control
• Investigating the impact of technology on human behavior and decision making
• Developing algorithms for efficient and accurate sound recognition
• Investigating the use of machine learning for predicting customer behavior
• Developing algorithms for efficient and secure data backup and recovery
• Investigating the impact of technology on education and learning outcomes
• Developing algorithms for efficient and accurate emotion recognition
• Investigating the use of machine learning for improving healthcare outcomes
• Developing algorithms for efficient and secure supply chain management
• Investigating the impact of technology on cultural and societal norms
• Developing algorithms for efficient and accurate gesture recognition
• Investigating the use of machine learning for predicting consumer demand
• Developing algorithms for efficient and secure cloud storage
• Investigating the impact of technology on environmental sustainability
• Developing algorithms for efficient and accurate voice recognition
• Investigating the use of machine learning for improving transportation systems
• Developing algorithms for efficient and secure mobile device management
• Investigating the impact of technology on social inequality and access to resources
• Machine learning for healthcare diagnosis and treatment
• Machine Learning for Cybersecurity
• Machine learning for personalized medicine
• Cybersecurity threats and defense strategies
• Big data analytics for business intelligence
• Blockchain technology and its applications
• Human-computer interaction in virtual reality environments
• Artificial intelligence for autonomous vehicles
• Natural language processing for chatbots
• Cloud computing and its impact on the IT industry
• Internet of Things (IoT) and smart homes
• Robotics and automation in manufacturing
• Augmented reality and its potential in education
• Data mining techniques for customer relationship management
• Computer vision for object recognition and tracking
• Quantum computing and its applications in cryptography
• Social media analytics and sentiment analysis
• Recommender systems for personalized content delivery
• Mobile computing and its impact on society
• Bioinformatics and genomic data analysis
• Deep learning for image and speech recognition
• Digital signal processing and audio processing algorithms
• Cloud storage and data security in the cloud
• Wearable technology and its impact on healthcare
• Computational linguistics for natural language understanding
• Cognitive computing for decision support systems
• Cyber-physical systems and their applications
• Edge computing and its impact on IoT
• Machine learning for fraud detection
• Cryptography and its role in secure communication
• Cybersecurity risks in the era of the Internet of Things
• Natural language generation for automated report writing
• 3D printing and its impact on manufacturing
• Virtual assistants and their applications in daily life
• Cloud-based gaming and its impact on the gaming industry
• Computer networks and their security issues
• Cyber forensics and its role in criminal investigations
• Machine learning for predictive maintenance in industrial settings
• Augmented reality for cultural heritage preservation
• Human-robot interaction and its applications
• Data visualization and its impact on decision-making
• Cybersecurity in financial systems and blockchain
• Computer graphics and animation techniques
• Biometrics and its role in secure authentication
• Cloud-based e-learning platforms and their impact on education
• Natural language processing for machine translation
• Machine learning for predictive maintenance in healthcare
• Cybersecurity and privacy issues in social media
• Computer vision for medical image analysis
• Natural language generation for content creation
• Cybersecurity challenges in cloud computing
• Human-robot collaboration in manufacturing
• Data mining for predicting customer churn
• Artificial intelligence for autonomous drones
• Cybersecurity risks in the healthcare industry
• Machine learning for speech synthesis
• Edge computing for low-latency applications
• Virtual reality for mental health therapy
• Quantum computing and its applications in finance
• Biomedical engineering and its applications
• Cybersecurity in autonomous systems
• Machine learning for predictive maintenance in transportation
• Computer vision for object detection in autonomous driving
• Augmented reality for industrial training and simulations
• Cloud-based cybersecurity solutions for small businesses
• Natural language processing for knowledge management
• Machine learning for personalized advertising
• Cybersecurity in the supply chain management
• Cybersecurity risks in the energy sector
• Computer vision for facial recognition
• Natural language processing for social media analysis
• Machine learning for sentiment analysis in customer reviews
• Explainable Artificial Intelligence
• Quantum Computing
• Blockchain Technology
• Human-Computer Interaction
• Natural Language Processing
• Cloud Computing
• Robotics and Automation
• Augmented Reality and Virtual Reality
• Cyber-Physical Systems
• Computational Neuroscience
• Big Data Analytics
• Computer Vision
• Cryptography and Network Security
• Internet of Things
• Computer Graphics and Visualization
• Artificial Intelligence for Game Design
• Computational Biology
• Social Network Analysis
• Bioinformatics
• Distributed Systems and Middleware
• Information Retrieval and Data Mining
• Computer Networks
• Mobile Computing and Wireless Networks
• Software Engineering
• Database Systems
• Parallel and Distributed Computing
• Human-Robot Interaction
• Intelligent Transportation Systems
• High-Performance Computing
• Cyber-Physical Security
• Deep Learning
• Sensor Networks
• Multi-Agent Systems
• Human-Centered Computing
• Wearable Computing
• Knowledge Representation and Reasoning
• Adaptive Systems
• Brain-Computer Interface
• Health Informatics
• Cognitive Computing
• Cybersecurity and Privacy
• Internet Security
• Cybercrime and Digital Forensics
• Cloud Security
• Cryptocurrencies and Digital Payments
• Machine Learning for Natural Language Generation
• Cognitive Robotics
• Neural Networks
• Semantic Web
• Image Processing
• Cyber Threat Intelligence
• Secure Mobile Computing
• Cybersecurity Education and Training
• Privacy Preserving Techniques
• Cyber-Physical Systems Security
• Virtualization and Containerization
• Machine Learning for Computer Vision
• Network Function Virtualization
• Cybersecurity Risk Management
• Information Security Governance
• Intrusion Detection and Prevention
• Biometric Authentication
• Machine Learning for Predictive Maintenance
• Security in Cloud-based Environments
• Cybersecurity for Industrial Control Systems
• Smart Grid Security
• Software Defined Networking
• Quantum Cryptography
• Security in the Internet of Things
• Natural language processing for sentiment analysis
• Blockchain technology for secure data sharing
• Developing efficient algorithms for big data analysis
• Cybersecurity for internet of things (IoT) devices
• Human-robot interaction for industrial automation
• Image recognition for autonomous vehicles
• Social media analytics for marketing strategy
• Quantum computing for solving complex problems
• Biometric authentication for secure access control
• Augmented reality for education and training
• Intelligent transportation systems for traffic management
• Predictive modeling for financial markets
• Cloud computing for scalable data storage and processing
• Virtual reality for therapy and mental health treatment
• Data visualization for business intelligence
• Recommender systems for personalized product recommendations
• Speech recognition for voice-controlled devices
• Mobile computing for real-time location-based services
• Neural networks for predicting user behavior
• Genetic algorithms for optimization problems
• Distributed computing for parallel processing
• Internet of things (IoT) for smart cities
• Wireless sensor networks for environmental monitoring
• Cloud-based gaming for high-performance gaming
• Social network analysis for identifying influencers
• Autonomous systems for agriculture
• Robotics for disaster response
• Data mining for customer segmentation
• Computer graphics for visual effects in movies and video games
• Virtual assistants for personalized customer service
• Natural language understanding for chatbots
• 3D printing for manufacturing prototypes
• Artificial intelligence for stock trading
• Machine learning for weather forecasting
• Biomedical engineering for prosthetics and implants
• Cybersecurity for financial institutions
• Machine learning for energy consumption optimization
• Computer vision for object tracking
• Natural language processing for document summarization
• Wearable technology for health and fitness monitoring
• Internet of things (IoT) for home automation
• Reinforcement learning for robotics control
• Big data analytics for customer insights
• Machine learning for supply chain optimization
• Natural language processing for legal document analysis
• Artificial intelligence for drug discovery
• Computer vision for object recognition in robotics
• Data mining for customer churn prediction
• Autonomous systems for space exploration
• Robotics for agriculture automation
• Machine learning for predicting earthquakes
• Natural language processing for sentiment analysis in customer reviews
• Big data analytics for predicting natural disasters
• Internet of things (IoT) for remote patient monitoring
• Blockchain technology for digital identity management
• Machine learning for predicting wildfire spread
• Computer vision for gesture recognition
• Natural language processing for automated translation
• Big data analytics for fraud detection in banking
• Internet of things (IoT) for smart homes
• Robotics for warehouse automation
• Machine learning for predicting air pollution
• Natural language processing for medical record analysis
• Augmented reality for architectural design
• Big data analytics for predicting traffic congestion
• Machine learning for predicting customer lifetime value
• Developing algorithms for efficient and accurate text recognition
• Natural Language Processing for Virtual Assistants
• Natural Language Processing for Sentiment Analysis in Social Media
• Explainable Artificial Intelligence (XAI) for Trust and Transparency
• Deep Learning for Image and Video Retrieval
• Edge Computing for Internet of Things (IoT) Applications
• Data Science for Social Media Analytics
• Cybersecurity for Critical Infrastructure Protection
• Natural Language Processing for Text Classification
• Quantum Computing for Optimization Problems
• Machine Learning for Personalized Health Monitoring
• Computer Vision for Autonomous Driving
• Blockchain Technology for Supply Chain Management
• Augmented Reality for Education and Training
• Natural Language Processing for Sentiment Analysis
• Machine Learning for Personalized Marketing
• Big Data Analytics for Financial Fraud Detection
• Cybersecurity for Cloud Security Assessment
• Artificial Intelligence for Natural Language Understanding
• Blockchain Technology for Decentralized Applications
• Virtual Reality for Cultural Heritage Preservation
• Natural Language Processing for Named Entity Recognition
• Machine Learning for Customer Churn Prediction
• Big Data Analytics for Social Network Analysis
• Cybersecurity for Intrusion Detection and Prevention
• Artificial Intelligence for Robotics and Automation
• Blockchain Technology for Digital Identity Management
• Virtual Reality for Rehabilitation and Therapy
• Natural Language Processing for Text Summarization
• Machine Learning for Credit Risk Assessment
• Big Data Analytics for Fraud Detection in Healthcare
• Cybersecurity for Internet Privacy Protection
• Artificial Intelligence for Game Design and Development
• Blockchain Technology for Decentralized Social Networks
• Virtual Reality for Marketing and Advertising
• Natural Language Processing for Opinion Mining
• Machine Learning for Anomaly Detection
• Big Data Analytics for Predictive Maintenance in Transportation
• Cybersecurity for Network Security Management
• Artificial Intelligence for Personalized News and Content Delivery
• Blockchain Technology for Cryptocurrency Mining
• Virtual Reality for Architectural Design and Visualization
• Natural Language Processing for Machine Translation
• Machine Learning for Automated Image Captioning
• Big Data Analytics for Stock Market Prediction
• Cybersecurity for Biometric Authentication Systems
• Artificial Intelligence for Human-Robot Interaction
• Blockchain Technology for Smart Grids
• Virtual Reality for Sports Training and Simulation
• Natural Language Processing for Question Answering Systems
• Machine Learning for Sentiment Analysis in Customer Feedback
• Big Data Analytics for Predictive Maintenance in Manufacturing
• Cybersecurity for Cloud-Based Systems
• Artificial Intelligence for Automated Journalism
• Blockchain Technology for Intellectual Property Management
• Virtual Reality for Therapy and Rehabilitation
• Natural Language Processing for Language Generation
• Machine Learning for Customer Lifetime Value Prediction
• Big Data Analytics for Predictive Maintenance in Energy Systems
• Cybersecurity for Secure Mobile Communication
• Artificial Intelligence for Emotion Recognition
• Blockchain Technology for Digital Asset Trading
• Virtual Reality for Automotive Design and Visualization
• Natural Language Processing for Semantic Web
• Machine Learning for Fraud Detection in Financial Transactions
• Big Data Analytics for Social Media Monitoring
• Cybersecurity for Cloud Storage and Sharing
• Artificial Intelligence for Personalized Education
• Blockchain Technology for Secure Online Voting Systems
• Virtual Reality for Cultural Tourism
• Natural Language Processing for Chatbot Communication
• Machine Learning for Medical Diagnosis and Treatment
• Big Data Analytics for Environmental Monitoring and Management.
• Cybersecurity for Cloud Computing Environments
• Virtual Reality for Training and Simulation
• Big Data Analytics for Sports Performance Analysis
• Cybersecurity for Internet of Things (IoT) Devices
• Artificial Intelligence for Traffic Management and Control
• Blockchain Technology for Smart Contracts
• Natural Language Processing for Document Summarization
• Machine Learning for Image and Video Recognition
• Blockchain Technology for Digital Asset Management
• Virtual Reality for Entertainment and Gaming
• Natural Language Processing for Opinion Mining in Online Reviews
• Machine Learning for Customer Relationship Management
• Big Data Analytics for Environmental Monitoring and Management
• Cybersecurity for Network Traffic Analysis and Monitoring
• Artificial Intelligence for Natural Language Generation
• Blockchain Technology for Supply Chain Transparency and Traceability
• Virtual Reality for Design and Visualization
• Natural Language Processing for Speech Recognition
• Machine Learning for Recommendation Systems
• Big Data Analytics for Customer Segmentation and Targeting
• Cybersecurity for Biometric Authentication
• Artificial Intelligence for Human-Computer Interaction
• Blockchain Technology for Decentralized Finance (DeFi)
• Virtual Reality for Tourism and Cultural Heritage
• Machine Learning for Cybersecurity Threat Detection and Prevention
• Big Data Analytics for Healthcare Cost Reduction
• Cybersecurity for Data Privacy and Protection
• Artificial Intelligence for Autonomous Vehicles
• Blockchain Technology for Cryptocurrency and Blockchain Security
• Virtual Reality for Real Estate Visualization
• Natural Language Processing for Question Answering
• Big Data Analytics for Financial Markets Prediction
• Cybersecurity for Cloud-Based Machine Learning Systems
• Artificial Intelligence for Personalized Advertising
• Blockchain Technology for Digital Identity Verification
• Virtual Reality for Cultural and Language Learning
• Natural Language Processing for Semantic Analysis
• Machine Learning for Business Forecasting
• Big Data Analytics for Social Media Marketing
• Artificial Intelligence for Content Generation
• Blockchain Technology for Smart Cities
• Virtual Reality for Historical Reconstruction
• Natural Language Processing for Knowledge Graph Construction
• Machine Learning for Speech Synthesis
• Big Data Analytics for Traffic Optimization
• Artificial Intelligence for Social Robotics
• Blockchain Technology for Healthcare Data Management
• Virtual Reality for Disaster Preparedness and Response
• Natural Language Processing for Multilingual Communication
• Machine Learning for Emotion Recognition
• Big Data Analytics for Human Resources Management
• Cybersecurity for Mobile App Security
• Artificial Intelligence for Financial Planning and Investment
• Blockchain Technology for Energy Management
• Virtual Reality for Cultural Preservation and Heritage.
• Big Data Analytics for Healthcare Management
• Cybersecurity in the Internet of Things (IoT)
• Artificial Intelligence for Predictive Maintenance
• Computational Biology for Drug Discovery
• Virtual Reality for Mental Health Treatment
• Machine Learning for Sentiment Analysis in Social Media
• Human-Computer Interaction for User Experience Design
• Cloud Computing for Disaster Recovery
• Quantum Computing for Cryptography
• Intelligent Transportation Systems for Smart Cities
• Cybersecurity for Autonomous Vehicles
• Artificial Intelligence for Fraud Detection in Financial Systems
• Social Network Analysis for Marketing Campaigns
• Cloud Computing for Video Game Streaming
• Machine Learning for Speech Recognition
• Augmented Reality for Architecture and Design
• Natural Language Processing for Customer Service Chatbots
• Machine Learning for Climate Change Prediction
• Big Data Analytics for Social Sciences
• Artificial Intelligence for Energy Management
• Virtual Reality for Tourism and Travel
• Cybersecurity for Smart Grids
• Machine Learning for Image Recognition
• Augmented Reality for Sports Training
• Natural Language Processing for Content Creation
• Cloud Computing for High-Performance Computing
• Artificial Intelligence for Personalized Medicine
• Virtual Reality for Architecture and Design
• Augmented Reality for Product Visualization
• Natural Language Processing for Language Translation
• Cybersecurity for Cloud Computing
• Artificial Intelligence for Supply Chain Optimization
• Blockchain Technology for Digital Voting Systems
• Virtual Reality for Job Training
• Augmented Reality for Retail Shopping
• Natural Language Processing for Sentiment Analysis in Customer Feedback
• Cloud Computing for Mobile Application Development
• Artificial Intelligence for Cybersecurity Threat Detection
• Blockchain Technology for Intellectual Property Protection
• Virtual Reality for Music Education
• Machine Learning for Financial Forecasting
• Augmented Reality for Medical Education
• Natural Language Processing for News Summarization
• Cybersecurity for Healthcare Data Protection
• Artificial Intelligence for Autonomous Robots
• Virtual Reality for Fitness and Health
• Machine Learning for Natural Language Understanding
• Augmented Reality for Museum Exhibits
• Natural Language Processing for Chatbot Personality Development
• Cloud Computing for Website Performance Optimization
• Artificial Intelligence for E-commerce Recommendation Systems
• Blockchain Technology for Supply Chain Traceability
• Virtual Reality for Military Training
• Augmented Reality for Advertising
• Natural Language Processing for Chatbot Conversation Management
• Cybersecurity for Cloud-Based Services
• Artificial Intelligence for Agricultural Management
• Blockchain Technology for Food Safety Assurance
• Virtual Reality for Historical Reenactments
• Machine Learning for Cybersecurity Incident Response.
• Secure Multiparty Computation
• Federated Learning
• Internet of Things Security
• Blockchain Scalability
• Quantum Computing Algorithms
• Explainable AI
• Data Privacy in the Age of Big Data
• Adversarial Machine Learning
• Deep Reinforcement Learning
• Online Learning and Streaming Algorithms
• Graph Neural Networks
• Automated Debugging and Fault Localization
• Mobile Application Development
• Software Engineering for Cloud Computing
• Cryptocurrency Security
• Edge Computing for Real-Time Applications
• Natural Language Generation
• Virtual and Augmented Reality
• Computational Biology and Bioinformatics
• Internet of Things Applications
• Robotics and Autonomous Systems
• Explainable Robotics
• 3D Printing and Additive Manufacturing
• Distributed Systems
• Parallel Computing
• Data Center Networking
• Data Mining and Knowledge Discovery
• Information Retrieval and Search Engines
• Network Security and Privacy
• Cloud Computing Security
• Data Analytics for Business Intelligence
• Neural Networks and Deep Learning
• Reinforcement Learning for Robotics
• Automated Planning and Scheduling
• Evolutionary Computation and Genetic Algorithms
• Formal Methods for Software Engineering
• Computational Complexity Theory
• Bio-inspired Computing
• Computer Vision for Object Recognition
• Automated Reasoning and Theorem Proving
• Natural Language Understanding
• Machine Learning for Healthcare
• Scalable Distributed Systems
• Sensor Networks and Internet of Things
• Smart Grids and Energy Systems
• Software Testing and Verification
• Web Application Security
• Wireless and Mobile Networks
• Computer Architecture and Hardware Design
• Digital Signal Processing
• Game Theory and Mechanism Design
• Multi-agent Systems
• Evolutionary Robotics
• Quantum Machine Learning
• Computational Social Science
• Explainable Recommender Systems.
• Artificial Intelligence and its applications
• Cloud computing and its benefits
• Cybersecurity threats and solutions
• Internet of Things and its impact on society
• Virtual and Augmented Reality and its uses
• Blockchain Technology and its potential in various industries
• Web Development and Design
• Digital Marketing and its effectiveness
• Big Data and Analytics
• Software Development Life Cycle
• Gaming Development and its growth
• Network Administration and Maintenance
• Machine Learning and its uses
• Data Warehousing and Mining
• Computer Architecture and Design
• Computer Graphics and Animation
• Quantum Computing and its potential
• Data Structures and Algorithms
• Computer Vision and Image Processing
• Robotics and its applications
• Operating Systems and its functions
• Information Theory and Coding
• Compiler Design and Optimization
• Computer Forensics and Cyber Crime Investigation
• Distributed Computing and its significance
• Artificial Neural Networks and Deep Learning
• Cloud Storage and Backup
• Programming Languages and their significance
• Computer Simulation and Modeling
• Computer Networks and its types
• Information Security and its types
• Computer-based Training and eLearning
• Medical Imaging and its uses
• Social Media Analysis and its applications
• Human Resource Information Systems
• Computer-Aided Design and Manufacturing
• Multimedia Systems and Applications
• Geographic Information Systems and its uses
• Computer-Assisted Language Learning
• Mobile Device Management and Security
• Data Compression and its types
• Knowledge Management Systems
• Text Mining and its uses
• Cyber Warfare and its consequences
• Wireless Networks and its advantages
• Computer Ethics and its importance
• Computational Linguistics and its applications
• Autonomous Systems and Robotics
• Information Visualization and its importance
• Geographic Information Retrieval and Mapping
• Business Intelligence and its benefits
• Digital Libraries and their significance
• Artificial Life and Evolutionary Computation
• Computer Music and its types
• Virtual Teams and Collaboration
• Computer Games and Learning
• Semantic Web and its applications
• Electronic Commerce and its advantages
• Multimedia Databases and their significance
• Computer Science Education and its importance
• Computer-Assisted Translation and Interpretation
• Ambient Intelligence and Smart Homes
• Autonomous Agents and Multi-Agent Systems.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

500+ Physics Research Topics

300+ Chemistry Research Topics

500+ Music Research Topics

500+ Environmental Research Topics

500+ Medical Research Topic Ideas

300+ Political Science Research Topics

Computer Science Thesis Topics

This page provides a comprehensive list of computer science thesis topics , carefully curated to support students in identifying and selecting innovative and relevant areas for their academic research. Whether you are at the beginning of your research journey or are seeking a specific area to explore further, this guide aims to serve as an essential resource. With an expansive array of topics spread across various sub-disciplines of computer science, this list is designed to meet a diverse range of interests and academic needs. From the complexities of artificial intelligence to the intricate designs of web development, each category is equipped with 40 specific topics, offering a breadth of possibilities to inspire your next big thesis project. Explore our guide to find not only a topic that resonates with your academic ambitions but also one that has the potential to contribute significantly to the field of computer science.

1000 Computer Science Thesis Topics and Ideas

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code, browse computer science thesis topics:, artificial intelligence thesis topics, augmented reality thesis topics, big data analytics thesis topics, bioinformatics thesis topics, blockchain technology thesis topics, cloud computing thesis topics, computer engineering thesis topics, computer vision thesis topics, cybersecurity thesis topics, data science thesis topics, digital transformation thesis topics, distributed systems and networks thesis topics, geographic information systems (gis) thesis topics, human-computer interaction (hci) thesis topics, image processing thesis topics, information system thesis topics, information technology thesis topics.

• Internet Of Things (IoT) Thesis Topics

Machine Learning Thesis Topics

Neural networks thesis topics, programming thesis topics, quantum computing thesis topics, robotics thesis topics, software engineering thesis topics, web development thesis topics.

• Ethical Implications of AI in Decision-Making Processes
• The Role of AI in Personalized Medicine: Opportunities and Challenges
• Advances in AI-Driven Predictive Analytics in Retail
• AI in Autonomous Vehicles: Safety, Regulation, and Technology Integration
• Natural Language Processing: Improving Human-Machine Interaction
• The Future of AI in Cybersecurity: Threats and Defenses
• Machine Learning Algorithms for Real-Time Data Processing
• AI and the Internet of Things: Transforming Smart Home Technology
• The Impact of Deep Learning on Image Recognition Technologies
• Reinforcement Learning: Applications in Robotics and Automation
• AI in Finance: Algorithmic Trading and Risk Assessment
• Bias and Fairness in AI: Addressing Socio-Technical Challenges
• The Evolution of AI in Education: Customized Learning Experiences
• AI for Environmental Conservation: Tracking and Predictive Analysis
• The Role of Artificial Neural Networks in Weather Forecasting
• AI in Agriculture: Predictive Analytics for Crop and Soil Management
• Emotional Recognition AI: Implications for Mental Health Assessments
• AI in Space Exploration: Autonomous Rovers and Mission Planning
• Enhancing User Experience with AI in Video Games
• AI-Powered Virtual Assistants: Trends, Effectiveness, and User Trust
• The Integration of AI in Traditional Industries: Case Studies
• Generative AI Models in Art and Creativity
• AI in LegalTech: Document Analysis and Litigation Prediction
• Healthcare Diagnostics: AI Applications in Radiology and Pathology
• AI and Blockchain: Enhancing Security in Decentralized Systems
• Ethics of AI in Surveillance: Privacy vs. Security
• AI in E-commerce: Personalization Engines and Customer Behavior Analysis
• The Future of AI in Telecommunications: Network Optimization and Service Delivery
• AI in Manufacturing: Predictive Maintenance and Quality Control
• Challenges of AI in Elderly Care: Ethical Considerations and Technological Solutions
• The Role of AI in Public Safety and Emergency Response
• AI for Content Creation: Impact on Media and Journalism
• AI-Driven Algorithms for Efficient Energy Management
• The Role of AI in Cultural Heritage Preservation
• AI and the Future of Public Transport: Optimization and Management
• Enhancing Sports Performance with AI-Based Analytics
• AI in Human Resources: Automating Recruitment and Employee Management
• Real-Time Translation AI: Breaking Language Barriers
• AI in Mental Health: Tools for Monitoring and Therapy Assistance
• The Future of AI Governance: Regulation and Standardization
• AR in Medical Training and Surgery Simulation
• The Impact of Augmented Reality in Retail: Enhancing Consumer Experience
• Augmented Reality for Enhanced Navigation Systems
• AR Applications in Maintenance and Repair in Industrial Settings
• The Role of AR in Enhancing Online Education
• Augmented Reality in Cultural Heritage: Interactive Visitor Experiences
• Developing AR Tools for Improved Sports Coaching and Training
• Privacy and Security Challenges in Augmented Reality Applications
• The Future of AR in Advertising: Engagement and Measurement
• User Interface Design for AR: Principles and Best Practices
• AR in Automotive Industry: Enhancing Driving Experience and Safety
• Augmented Reality for Emergency Response Training
• AR and IoT: Converging Technologies for Smart Environments
• Enhancing Physical Rehabilitation with AR Applications
• The Role of AR in Enhancing Public Safety and Awareness
• Augmented Reality in Fashion: Virtual Fitting and Personalized Shopping
• AR for Environmental Education: Interactive and Immersive Learning
• The Use of AR in Building and Architecture Planning
• AR in the Entertainment Industry: Games and Live Events
• Implementing AR in Museums and Art Galleries for Interactive Learning
• Augmented Reality for Real Estate: Virtual Tours and Property Visualization
• AR in Consumer Electronics: Integration in Smart Devices
• The Development of AR Applications for Children’s Education
• AR for Enhancing User Engagement in Social Media Platforms
• The Application of AR in Field Service Management
• Augmented Reality for Disaster Management and Risk Assessment
• Challenges of Content Creation for Augmented Reality
• Future Trends in AR Hardware: Wearables and Beyond
• Legal and Ethical Considerations of Augmented Reality Technology
• AR in Space Exploration: Tools for Simulation and Training
• Interactive Shopping Experiences with AR: The Future of Retail
• AR in Wildlife Conservation: Educational Tools and Awareness
• The Impact of AR on the Publishing Industry: Interactive Books and Magazines
• Augmented Reality and Its Role in Automotive Manufacturing
• AR for Job Training: Bridging the Skill Gap in Various Industries
• The Role of AR in Therapy: New Frontiers in Mental Health Treatment
• The Future of Augmented Reality in Sports Broadcasting
• AR as a Tool for Enhancing Public Art Installations
• Augmented Reality in the Tourism Industry: Personalized Travel Experiences
• The Use of AR in Security Training: Realistic and Safe Simulations
• The Role of Big Data in Improving Healthcare Outcomes
• Big Data and Its Impact on Consumer Behavior Analysis
• Privacy Concerns in Big Data: Ethical and Legal Implications
• The Application of Big Data in Predictive Maintenance for Manufacturing
• Real-Time Big Data Processing: Tools and Techniques
• Big Data in Financial Services: Fraud Detection and Risk Management
• The Evolution of Big Data Technologies: From Hadoop to Spark
• Big Data Visualization: Techniques for Effective Communication of Insights
• The Integration of Big Data and Artificial Intelligence
• Big Data in Smart Cities: Applications in Traffic Management and Energy Use
• Enhancing Supply Chain Efficiency with Big Data Analytics
• Big Data in Sports Analytics: Improving Team Performance and Fan Engagement
• The Role of Big Data in Environmental Monitoring and Sustainability
• Big Data and Social Media: Analyzing Sentiments and Trends
• Scalability Challenges in Big Data Systems
• The Future of Big Data in Retail: Personalization and Customer Experience
• Big Data in Education: Customized Learning Paths and Student Performance Analysis
• Privacy-Preserving Techniques in Big Data
• Big Data in Public Health: Epidemiology and Disease Surveillance
• The Impact of Big Data on Insurance: Tailored Policies and Pricing
• Edge Computing in Big Data: Processing at the Source
• Big Data and the Internet of Things: Generating Insights from IoT Data
• Cloud-Based Big Data Analytics: Opportunities and Challenges
• Big Data Governance: Policies, Standards, and Management
• The Role of Big Data in Crisis Management and Response
• Machine Learning with Big Data: Building Predictive Models
• Big Data in Agriculture: Precision Farming and Yield Optimization
• The Ethics of Big Data in Research: Consent and Anonymity
• Cross-Domain Big Data Integration: Challenges and Solutions
• Big Data and Cybersecurity: Threat Detection and Prevention Strategies
• Real-Time Streaming Analytics in Big Data
• Big Data in the Media Industry: Content Optimization and Viewer Insights
• The Impact of GDPR on Big Data Practices
• Quantum Computing and Big Data: Future Prospects
• Big Data in E-Commerce: Optimizing Logistics and Inventory Management
• Big Data Talent: Education and Skill Development for Data Scientists
• The Role of Big Data in Political Campaigns and Voting Behavior Analysis
• Big Data and Mental Health: Analyzing Patterns for Better Interventions
• Big Data in Genomics and Personalized Medicine
• The Future of Big Data in Autonomous Driving Technologies
• The Role of Bioinformatics in Personalized Medicine
• Next-Generation Sequencing Data Analysis: Challenges and Opportunities
• Bioinformatics and the Study of Genetic Diseases
• Computational Models for Understanding Protein Structure and Function
• Bioinformatics in Drug Discovery and Development
• The Impact of Big Data on Bioinformatics: Data Management and Analysis
• Machine Learning Applications in Bioinformatics
• Bioinformatics Approaches for Cancer Genomics
• The Development of Bioinformatics Tools for Metagenomics Analysis
• Ethical Considerations in Bioinformatics: Data Sharing and Privacy
• The Role of Bioinformatics in Agricultural Biotechnology
• Bioinformatics and Viral Evolution: Tracking Pathogens and Outbreaks
• The Integration of Bioinformatics and Systems Biology
• Bioinformatics in Neuroscience: Mapping the Brain
• The Future of Bioinformatics in Non-Invasive Prenatal Testing
• Bioinformatics and the Human Microbiome: Health Implications
• The Application of Artificial Intelligence in Bioinformatics
• Structural Bioinformatics: Computational Techniques for Molecular Modeling
• Comparative Genomics: Insights into Evolution and Function
• Bioinformatics in Immunology: Vaccine Design and Immune Response Analysis
• High-Performance Computing in Bioinformatics
• The Challenge of Proteomics in Bioinformatics
• RNA-Seq Data Analysis and Interpretation
• Cloud Computing Solutions for Bioinformatics Data
• Computational Epigenetics: DNA Methylation and Histone Modification Analysis
• Bioinformatics in Ecology: Biodiversity and Conservation Genetics
• The Role of Bioinformatics in Forensic Analysis
• Mobile Apps and Tools for Bioinformatics Research
• Bioinformatics and Public Health: Epidemiological Studies
• The Use of Bioinformatics in Clinical Diagnostics
• Genetic Algorithms in Bioinformatics
• Bioinformatics for Aging Research: Understanding the Mechanisms of Aging
• Data Visualization Techniques in Bioinformatics
• Bioinformatics and the Development of Therapeutic Antibodies
• The Role of Bioinformatics in Stem Cell Research
• Bioinformatics and Cardiovascular Diseases: Genomic Insights
• The Impact of Machine Learning on Functional Genomics in Bioinformatics
• Bioinformatics in Dental Research: Genetic Links to Oral Diseases
• The Future of CRISPR Technology and Bioinformatics
• Bioinformatics and Nutrition: Genomic Insights into Diet and Health
• Blockchain for Enhancing Cybersecurity in Various Industries
• The Impact of Blockchain on Supply Chain Transparency
• Blockchain in Healthcare: Patient Data Management and Security
• The Application of Blockchain in Voting Systems
• Blockchain and Smart Contracts: Legal Implications and Applications
• Cryptocurrencies: Market Trends and the Future of Digital Finance
• Blockchain in Real Estate: Improving Property and Land Registration
• The Role of Blockchain in Managing Digital Identities
• Blockchain for Intellectual Property Management
• Energy Sector Innovations: Blockchain for Renewable Energy Distribution
• Blockchain and the Future of Public Sector Operations
• The Impact of Blockchain on Cross-Border Payments
• Blockchain for Non-Fungible Tokens (NFTs): Applications in Art and Media
• Privacy Issues in Blockchain Applications
• Blockchain in the Automotive Industry: Supply Chain and Beyond
• Decentralized Finance (DeFi): Opportunities and Challenges
• The Role of Blockchain in Combating Counterfeiting and Fraud
• Blockchain for Sustainable Environmental Practices
• The Integration of Artificial Intelligence with Blockchain
• Blockchain Education: Curriculum Development and Training Needs
• Blockchain in the Music Industry: Rights Management and Revenue Distribution
• The Challenges of Blockchain Scalability and Performance Optimization
• The Future of Blockchain in the Telecommunications Industry
• Blockchain and Consumer Data Privacy: A New Paradigm
• Blockchain for Disaster Recovery and Business Continuity
• Blockchain in the Charity and Non-Profit Sectors
• Quantum Resistance in Blockchain: Preparing for the Quantum Era
• Blockchain and Its Impact on Traditional Banking and Financial Institutions
• Legal and Regulatory Challenges Facing Blockchain Technology
• Blockchain for Improved Logistics and Freight Management
• The Role of Blockchain in the Evolution of the Internet of Things (IoT)
• Blockchain and the Future of Gaming: Transparency and Fair Play
• Blockchain for Academic Credentials Verification
• The Application of Blockchain in the Insurance Industry
• Blockchain and the Future of Content Creation and Distribution
• Blockchain for Enhancing Data Integrity in Scientific Research
• The Impact of Blockchain on Human Resources: Employee Verification and Salary Payments
• Blockchain and the Future of Retail: Customer Loyalty Programs and Inventory Management
• Blockchain and Industrial Automation: Trust and Efficiency
• Blockchain for Digital Marketing: Transparency and Consumer Engagement
• Multi-Cloud Strategies: Optimization and Security Challenges
• Advances in Cloud Computing Architectures for Scalable Applications
• Edge Computing: Extending the Reach of Cloud Services
• Cloud Security: Novel Approaches to Data Encryption and Threat Mitigation
• The Impact of Serverless Computing on Software Development Lifecycle
• Cloud Computing and Sustainability: Energy-Efficient Data Centers
• Cloud Service Models: Comparative Analysis of IaaS, PaaS, and SaaS
• Cloud Migration Strategies: Best Practices and Common Pitfalls
• The Role of Cloud Computing in Big Data Analytics
• Implementing AI and Machine Learning Workloads on Cloud Platforms
• Hybrid Cloud Environments: Management Tools and Techniques
• Cloud Computing in Healthcare: Compliance, Security, and Use Cases
• Cost-Effective Cloud Solutions for Small and Medium Enterprises (SMEs)
• The Evolution of Cloud Storage Solutions: Trends and Technologies
• Cloud-Based Disaster Recovery Solutions: Design and Reliability
• Blockchain in Cloud Services: Enhancing Transparency and Trust
• Cloud Networking: Managing Connectivity and Traffic in Cloud Environments
• Cloud Governance: Managing Compliance and Operational Risks
• The Future of Cloud Computing: Quantum Computing Integration
• Performance Benchmarking of Cloud Services Across Different Providers
• Privacy Preservation in Cloud Environments
• Cloud Computing in Education: Virtual Classrooms and Learning Management Systems
• Automation in Cloud Deployments: Tools and Strategies
• Cloud Auditing and Monitoring Techniques
• Mobile Cloud Computing: Challenges and Future Trends
• The Role of Cloud Computing in Digital Media Production and Distribution
• Security Risks in Multi-Tenancy Cloud Environments
• Cloud Computing for Scientific Research: Enabling Complex Simulations
• The Impact of 5G on Cloud Computing Services
• Federated Clouds: Building Collaborative Cloud Environments
• Managing Software Dependencies in Cloud Applications
• The Economics of Cloud Computing: Cost Models and Pricing Strategies
• Cloud Computing in Government: Security Protocols and Citizen Services
• Cloud Access Security Brokers (CASBs): Security Enforcement Points
• DevOps in the Cloud: Strategies for Continuous Integration and Deployment
• Predictive Analytics in Cloud Computing
• The Role of Cloud Computing in IoT Deployment
• Implementing Robust Cybersecurity Measures in Cloud Architecture
• Cloud Computing in the Financial Sector: Handling Sensitive Data
• Future Trends in Cloud Computing: The Role of AI in Cloud Optimization
• Advances in Microprocessor Design and Architecture
• FPGA-Based Design: Innovations and Applications
• The Role of Embedded Systems in Consumer Electronics
• Quantum Computing: Hardware Development and Challenges
• High-Performance Computing (HPC) and Parallel Processing
• Design and Analysis of Computer Networks
• Cyber-Physical Systems: Design, Analysis, and Security
• The Impact of Nanotechnology on Computer Hardware
• Wireless Sensor Networks: Design and Optimization
• Cryptographic Hardware: Implementations and Security Evaluations
• Machine Learning Techniques for Hardware Optimization
• Hardware for Artificial Intelligence: GPUs vs. TPUs
• Energy-Efficient Hardware Designs for Sustainable Computing
• Security Aspects of Mobile and Ubiquitous Computing
• Advanced Algorithms for Computer-Aided Design (CAD) of VLSI
• Signal Processing in Communication Systems
• The Development of Wearable Computing Devices
• Computer Hardware Testing: Techniques and Tools
• The Role of Hardware in Network Security
• The Evolution of Interface Designs in Consumer Electronics
• Biometric Systems: Hardware and Software Integration
• The Integration of IoT Devices in Smart Environments
• Electronic Design Automation (EDA) Tools and Methodologies
• Robotics: Hardware Design and Control Systems
• Hardware Accelerators for Deep Learning Applications
• Developments in Non-Volatile Memory Technologies
• The Future of Computer Hardware in the Era of Quantum Computing
• Hardware Solutions for Data Storage and Retrieval
• Power Management Techniques in Embedded Systems
• Challenges in Designing Multi-Core Processors
• System on Chip (SoC) Design Trends and Challenges
• The Role of Computer Engineering in Aerospace Technology
• Real-Time Systems: Design and Implementation Challenges
• Hardware Support for Virtualization Technology
• Advances in Computer Graphics Hardware
• The Impact of 5G Technology on Mobile Computing Hardware
• Environmental Impact Assessment of Computer Hardware Production
• Security Vulnerabilities in Modern Microprocessors
• Computer Hardware Innovations in the Automotive Industry
• The Role of Computer Engineering in Medical Device Technology
• Deep Learning Approaches to Object Recognition
• Real-Time Image Processing for Autonomous Vehicles
• Computer Vision in Robotic Surgery: Techniques and Challenges
• Facial Recognition Technology: Innovations and Privacy Concerns
• Machine Vision in Industrial Automation and Quality Control
• 3D Reconstruction Techniques in Computer Vision
• Enhancing Sports Analytics with Computer Vision
• Augmented Reality: Integrating Computer Vision for Immersive Experiences
• Computer Vision for Environmental Monitoring
• Thermal Imaging and Its Applications in Computer Vision
• Computer Vision in Retail: Customer Behavior and Store Layout Optimization
• Motion Detection and Tracking in Security Systems
• The Role of Computer Vision in Content Moderation on Social Media
• Gesture Recognition: Methods and Applications
• Computer Vision in Agriculture: Pest Detection and Crop Analysis
• Advances in Medical Imaging: Machine Learning and Computer Vision
• Scene Understanding and Contextual Inference in Images
• The Development of Vision-Based Autonomous Drones
• Optical Character Recognition (OCR): Latest Techniques and Applications
• The Impact of Computer Vision on Virtual Reality Experiences
• Biometrics: Enhancing Security Systems with Computer Vision
• Computer Vision for Wildlife Conservation: Species Recognition and Behavior Analysis
• Underwater Image Processing: Challenges and Techniques
• Video Surveillance: The Evolution of Algorithmic Approaches
• Advanced Driver-Assistance Systems (ADAS): Leveraging Computer Vision
• Computational Photography: Enhancing Image Capture Techniques
• The Integration of AI in Computer Vision: Ethical and Technical Considerations
• Computer Vision in the Gaming Industry: From Design to Interaction
• The Future of Computer Vision in Smart Cities
• Pattern Recognition in Historical Document Analysis
• The Role of Computer Vision in the Manufacturing of Customized Products
• Enhancing Accessibility with Computer Vision: Tools for the Visually Impaired
• The Use of Computer Vision in Behavioral Research
• Predictive Analytics with Computer Vision in Sports
• Image Synthesis with Generative Adversarial Networks (GANs)
• The Use of Computer Vision in Remote Sensing
• Real-Time Video Analytics for Public Safety
• The Role of Computer Vision in Telemedicine
• Computer Vision and the Internet of Things (IoT): A Synergistic Approach
• Future Trends in Computer Vision: Quantum Computing and Beyond
• Advances in Cryptography: Post-Quantum Cryptosystems
• Artificial Intelligence in Cybersecurity: Threat Detection and Response
• Blockchain for Enhanced Security in Distributed Networks
• The Impact of IoT on Cybersecurity: Vulnerabilities and Solutions
• Cybersecurity in Cloud Computing: Best Practices and Tools
• Ethical Hacking: Techniques and Ethical Implications
• The Role of Human Factors in Cybersecurity Breaches
• Privacy-preserving Technologies in an Age of Surveillance
• The Evolution of Ransomware Attacks and Defense Strategies
• Secure Software Development: Integrating Security in DevOps (DevSecOps)
• Cybersecurity in Critical Infrastructure: Challenges and Innovations
• The Future of Biometric Security Systems
• Cyber Warfare: State-sponsored Attacks and Defense Mechanisms
• The Role of Cybersecurity in Protecting Digital Identities
• Social Engineering Attacks: Prevention and Countermeasures
• Mobile Security: Protecting Against Malware and Exploits
• Wireless Network Security: Protocols and Practices
• Data Breaches: Analysis, Consequences, and Mitigation
• The Ethics of Cybersecurity: Balancing Privacy and Security
• Regulatory Compliance and Cybersecurity: GDPR and Beyond
• The Impact of 5G Technology on Cybersecurity
• The Role of Machine Learning in Cyber Threat Intelligence
• Cybersecurity in Automotive Systems: Challenges in a Connected Environment
• The Use of Virtual Reality for Cybersecurity Training and Simulation
• Advanced Persistent Threats (APT): Detection and Response
• Cybersecurity for Smart Cities: Challenges and Solutions
• Deep Learning Applications in Malware Detection
• The Role of Cybersecurity in Healthcare: Protecting Patient Data
• Supply Chain Cybersecurity: Identifying Risks and Solutions
• Endpoint Security: Trends, Challenges, and Future Directions
• Forensic Techniques in Cybersecurity: Tracking and Analyzing Cyber Crimes
• The Influence of International Law on Cyber Operations
• Protecting Financial Institutions from Cyber Frauds and Attacks
• Quantum Computing and Its Implications for Cybersecurity
• Cybersecurity and Remote Work: Emerging Threats and Strategies
• IoT Security in Industrial Applications
• Cyber Insurance: Risk Assessment and Management
• Security Challenges in Edge Computing Environments
• Anomaly Detection in Network Security Using AI Techniques
• Securing the Software Supply Chain in Application Development
• Big Data Analytics: Techniques and Applications in Real-time
• Machine Learning Algorithms for Predictive Analytics
• Data Science in Healthcare: Improving Patient Outcomes with Predictive Models
• The Role of Data Science in Financial Market Predictions
• Natural Language Processing: Emerging Trends and Applications
• Data Visualization Tools and Techniques for Enhanced Business Intelligence
• Ethics in Data Science: Privacy, Fairness, and Transparency
• The Use of Data Science in Environmental Science for Sustainability Studies
• The Impact of Data Science on Social Media Marketing Strategies
• Data Mining Techniques for Detecting Patterns in Large Datasets
• AI and Data Science: Synergies and Future Prospects
• Reinforcement Learning: Applications and Challenges in Data Science
• The Role of Data Science in E-commerce Personalization
• Predictive Maintenance in Manufacturing Through Data Science
• The Evolution of Recommendation Systems in Streaming Services
• Real-time Data Processing with Stream Analytics
• Deep Learning for Image and Video Analysis
• Data Governance in Big Data Analytics
• Text Analytics and Sentiment Analysis for Customer Feedback
• Fraud Detection in Banking and Insurance Using Data Science
• The Integration of IoT Data in Data Science Models
• The Future of Data Science in Quantum Computing
• Data Science for Public Health: Epidemic Outbreak Prediction
• Sports Analytics: Performance Improvement and Injury Prevention
• Data Science in Retail: Inventory Management and Customer Journey Analysis
• Data Science in Smart Cities: Traffic and Urban Planning
• The Use of Blockchain in Data Security and Integrity
• Geospatial Analysis for Environmental Monitoring
• Time Series Analysis in Economic Forecasting
• Data Science in Education: Analyzing Trends and Student Performance
• Predictive Policing: Data Science in Law Enforcement
• Data Science in Agriculture: Yield Prediction and Soil Health
• Computational Social Science: Analyzing Societal Trends
• Data Science in Energy Sector: Consumption and Optimization
• Personalization Technologies in Healthcare Through Data Science
• The Role of Data Science in Content Creation and Media
• Anomaly Detection in Network Security Using Data Science Techniques
• The Future of Autonomous Vehicles: Data Science-Driven Innovations
• Multimodal Data Fusion Techniques in Data Science
• Scalability Challenges in Data Science Projects
• The Role of Digital Transformation in Business Model Innovation
• The Impact of Digital Technologies on Customer Experience
• Digital Transformation in the Banking Sector: Trends and Challenges
• The Use of AI and Robotics in Digital Transformation of Manufacturing
• Digital Transformation in Healthcare: Telemedicine and Beyond
• The Influence of Big Data on Decision-Making Processes in Corporations
• Blockchain as a Driver for Transparency in Digital Transformation
• The Role of IoT in Enhancing Operational Efficiency in Industries
• Digital Marketing Strategies: SEO, Content, and Social Media
• The Integration of Cyber-Physical Systems in Industrial Automation
• Digital Transformation in Education: Virtual Learning Environments
• Smart Cities: The Role of Digital Technologies in Urban Planning
• Digital Transformation in the Retail Sector: E-commerce Evolution
• The Future of Work: Impact of Digital Transformation on Workplaces
• Cybersecurity Challenges in a Digitally Transformed World
• Mobile Technologies and Their Impact on Digital Transformation
• The Role of Digital Twin Technology in Industry 4.0
• Digital Transformation in the Public Sector: E-Government Services
• Data Privacy and Security in the Age of Digital Transformation
• Digital Transformation in the Energy Sector: Smart Grids and Renewable Energy
• The Use of Augmented Reality in Training and Development
• The Role of Virtual Reality in Real Estate and Architecture
• Digital Transformation and Sustainability: Reducing Environmental Footprint
• The Role of Digital Transformation in Supply Chain Optimization
• Digital Transformation in Agriculture: IoT and Smart Farming
• The Impact of 5G on Digital Transformation Initiatives
• The Influence of Digital Transformation on Media and Entertainment
• Digital Transformation in Insurance: Telematics and Risk Assessment
• The Role of AI in Enhancing Customer Service Operations
• The Future of Digital Transformation: Trends and Predictions
• Digital Transformation and Corporate Governance
• The Role of Leadership in Driving Digital Transformation
• Digital Transformation in Non-Profit Organizations: Challenges and Benefits
• The Economic Implications of Digital Transformation
• The Cultural Impact of Digital Transformation on Organizations
• Digital Transformation in Transportation: Logistics and Fleet Management
• User Experience (UX) Design in Digital Transformation
• The Role of Digital Transformation in Crisis Management
• Digital Transformation and Human Resource Management
• Implementing Change Management in Digital Transformation Projects
• Scalability Challenges in Distributed Systems: Solutions and Strategies
• Blockchain Technology: Enhancing Security and Transparency in Distributed Networks
• The Role of Edge Computing in Distributed Systems
• Designing Fault-Tolerant Systems in Distributed Networks
• The Impact of 5G Technology on Distributed Network Architectures
• Machine Learning Algorithms for Network Traffic Analysis
• Load Balancing Techniques in Distributed Computing
• The Use of Distributed Ledger Technology Beyond Cryptocurrencies
• Network Function Virtualization (NFV) and Its Impact on Service Providers
• The Evolution of Software-Defined Networking (SDN) in Enterprise Environments
• Implementing Robust Cybersecurity Measures in Distributed Systems
• Quantum Computing: Implications for Network Security in Distributed Systems
• Peer-to-Peer Network Protocols and Their Applications
• The Internet of Things (IoT): Network Challenges and Communication Protocols
• Real-Time Data Processing in Distributed Sensor Networks
• The Role of Artificial Intelligence in Optimizing Network Operations
• Privacy and Data Protection Strategies in Distributed Systems
• The Future of Distributed Computing in Cloud Environments
• Energy Efficiency in Distributed Network Systems
• Wireless Mesh Networks: Design, Challenges, and Applications
• Multi-Access Edge Computing (MEC): Use Cases and Deployment Challenges
• Consensus Algorithms in Distributed Systems: From Blockchain to New Applications
• The Use of Containers and Microservices in Building Scalable Applications
• Network Slicing for 5G: Opportunities and Challenges
• The Role of Distributed Systems in Big Data Analytics
• Managing Data Consistency in Distributed Databases
• The Impact of Distributed Systems on Digital Transformation Strategies
• Augmented Reality over Distributed Networks: Performance and Scalability Issues
• The Application of Distributed Systems in Smart Grid Technology
• Developing Distributed Applications Using Serverless Architectures
• The Challenges of Implementing IPv6 in Distributed Networks
• Distributed Systems for Disaster Recovery: Design and Implementation
• The Use of Virtual Reality in Distributed Network Environments
• Security Protocols for Ad Hoc Networks in Emergency Situations
• The Role of Distributed Networks in Enhancing Mobile Broadband Services
• Next-Generation Protocols for Enhanced Network Reliability and Performance
• The Application of Blockchain in Securing Distributed IoT Networks
• Dynamic Resource Allocation Strategies in Distributed Systems
• The Integration of Distributed Systems with Existing IT Infrastructure
• The Future of Autonomous Systems in Distributed Networking
• The Integration of GIS with Remote Sensing for Environmental Monitoring
• GIS in Urban Planning: Techniques for Sustainable Development
• The Role of GIS in Disaster Management and Response Strategies
• Real-Time GIS Applications in Traffic Management and Route Planning
• The Use of GIS in Water Resource Management
• GIS and Public Health: Tracking Epidemics and Healthcare Access
• Advances in 3D GIS: Technologies and Applications
• GIS in Agricultural Management: Precision Farming Techniques
• The Impact of GIS on Biodiversity Conservation Efforts
• Spatial Data Analysis for Crime Pattern Detection and Prevention
• GIS in Renewable Energy: Site Selection and Resource Management
• The Role of GIS in Historical Research and Archaeology
• GIS and Machine Learning: Integrating Spatial Analysis with Predictive Models
• Cloud Computing and GIS: Enhancing Accessibility and Data Processing
• The Application of GIS in Managing Public Transportation Systems
• GIS in Real Estate: Market Analysis and Property Valuation
• The Use of GIS for Environmental Impact Assessments
• Mobile GIS Applications: Development and Usage Trends
• GIS and Its Role in Smart City Initiatives
• Privacy Issues in the Use of Geographic Information Systems
• GIS in Forest Management: Monitoring and Conservation Strategies
• The Impact of GIS on Tourism: Enhancing Visitor Experiences through Technology
• GIS in the Insurance Industry: Risk Assessment and Policy Design
• The Development of Participatory GIS (PGIS) for Community Engagement
• GIS in Coastal Management: Addressing Erosion and Flood Risks
• Geospatial Analytics in Retail: Optimizing Location and Consumer Insights
• GIS for Wildlife Tracking and Habitat Analysis
• The Use of GIS in Climate Change Studies
• GIS and Social Media: Analyzing Spatial Trends from User Data
• The Future of GIS: Augmented Reality and Virtual Reality Applications
• GIS in Education: Tools for Teaching Geographic Concepts
• The Role of GIS in Land Use Planning and Zoning
• GIS for Emergency Medical Services: Optimizing Response Times
• Open Source GIS Software: Development and Community Contributions
• GIS and the Internet of Things (IoT): Converging Technologies for Advanced Monitoring
• GIS for Mineral Exploration: Techniques and Applications
• The Role of GIS in Municipal Management and Services
• GIS and Drone Technology: A Synergy for Precision Mapping
• Spatial Statistics in GIS: Techniques for Advanced Data Analysis
• Future Trends in GIS: The Integration of AI for Smarter Solutions
• The Evolution of User Interface (UI) Design: From Desktop to Mobile and Beyond
• The Role of HCI in Enhancing Accessibility for Disabled Users
• Virtual Reality (VR) and Augmented Reality (AR) in HCI: New Dimensions of Interaction
• The Impact of HCI on User Experience (UX) in Software Applications
• Cognitive Aspects of HCI: Understanding User Perception and Behavior
• HCI and the Internet of Things (IoT): Designing Interactive Smart Devices
• The Use of Biometrics in HCI: Security and Usability Concerns
• HCI in Educational Technologies: Enhancing Learning through Interaction
• Emotional Recognition and Its Application in HCI
• The Role of HCI in Wearable Technology: Design and Functionality
• Advanced Techniques in Voice User Interfaces (VUIs)
• The Impact of HCI on Social Media Interaction Patterns
• HCI in Healthcare: Designing User-Friendly Medical Devices and Software
• HCI and Gaming: Enhancing Player Engagement and Experience
• The Use of HCI in Robotic Systems: Improving Human-Robot Interaction
• The Influence of HCI on E-commerce: Optimizing User Journeys and Conversions
• HCI in Smart Homes: Interaction Design for Automated Environments
• Multimodal Interaction: Integrating Touch, Voice, and Gesture in HCI
• HCI and Aging: Designing Technology for Older Adults
• The Role of HCI in Virtual Teams: Tools and Strategies for Collaboration
• User-Centered Design: HCI Strategies for Developing User-Focused Software
• HCI Research Methodologies: Experimental Design and User Studies
• The Application of HCI Principles in the Design of Public Kiosks
• The Future of HCI: Integrating Artificial Intelligence for Smarter Interfaces
• HCI in Transportation: Designing User Interfaces for Autonomous Vehicles
• Privacy and Ethics in HCI: Addressing User Data Security
• HCI and Environmental Sustainability: Promoting Eco-Friendly Behaviors
• Adaptive Interfaces: HCI Design for Personalized User Experiences
• The Role of HCI in Content Creation: Tools for Artists and Designers
• HCI for Crisis Management: Designing Systems for Emergency Use
• The Use of HCI in Sports Technology: Enhancing Training and Performance
• The Evolution of Haptic Feedback in HCI
• HCI and Cultural Differences: Designing for Global User Bases
• The Impact of HCI on Digital Marketing: Creating Engaging User Interactions
• HCI in Financial Services: Improving User Interfaces for Banking Apps
• The Role of HCI in Enhancing User Trust in Technology
• HCI for Public Safety: User Interfaces for Security Systems
• The Application of HCI in the Film and Television Industry
• HCI and the Future of Work: Designing Interfaces for Remote Collaboration
• Innovations in HCI: Exploring New Interaction Technologies and Their Applications
• Deep Learning Techniques for Advanced Image Segmentation
• Real-Time Image Processing for Autonomous Driving Systems
• Image Enhancement Algorithms for Underwater Imaging
• Super-Resolution Imaging: Techniques and Applications
• The Role of Image Processing in Remote Sensing and Satellite Imagery Analysis
• Machine Learning Models for Medical Image Diagnosis
• The Impact of AI on Photographic Restoration and Enhancement
• Image Processing in Security Systems: Facial Recognition and Motion Detection
• Advanced Algorithms for Image Noise Reduction
• 3D Image Reconstruction Techniques in Tomography
• Image Processing for Agricultural Monitoring: Crop Disease Detection and Yield Prediction
• Techniques for Panoramic Image Stitching
• Video Image Processing: Real-Time Streaming and Data Compression
• The Application of Image Processing in Printing Technology
• Color Image Processing: Theory and Practical Applications
• The Use of Image Processing in Biometrics Identification
• Computational Photography: Image Processing Techniques in Smartphone Cameras
• Image Processing for Augmented Reality: Real-time Object Overlay
• The Development of Image Processing Algorithms for Traffic Control Systems
• Pattern Recognition and Analysis in Forensic Imaging
• Adaptive Filtering Techniques in Image Processing
• Image Processing in Retail: Customer Tracking and Behavior Analysis
• The Role of Image Processing in Cultural Heritage Preservation
• Image Segmentation Techniques for Cancer Detection in Medical Imaging
• High Dynamic Range (HDR) Imaging: Algorithms and Display Techniques
• Image Classification with Deep Convolutional Neural Networks
• The Evolution of Edge Detection Algorithms in Image Processing
• Image Processing for Wildlife Monitoring: Species Recognition and Behavior Analysis
• Application of Wavelet Transforms in Image Compression
• Image Processing in Sports: Enhancing Broadcasts and Performance Analysis
• Optical Character Recognition (OCR) Improvements in Document Scanning
• Multi-Spectral Imaging for Environmental and Earth Studies
• Image Processing for Space Exploration: Analysis of Planetary Images
• Real-Time Image Processing for Event Surveillance
• The Influence of Quantum Computing on Image Processing Speed and Security
• Machine Vision in Manufacturing: Defect Detection and Quality Control
• Image Processing in Neurology: Visualizing Brain Functions
• Photogrammetry and Image Processing in Geology: 3D Terrain Mapping
• Advanced Techniques in Image Watermarking for Copyright Protection
• The Future of Image Processing: Integrating AI for Automated Editing
• The Evolution of Enterprise Resource Planning (ERP) Systems in the Digital Age
• Information Systems for Managing Distributed Workforces
• The Role of Information Systems in Enhancing Supply Chain Management
• Cybersecurity Measures in Information Systems
• The Impact of Big Data on Decision Support Systems
• Blockchain Technology for Information System Security
• The Development of Sustainable IT Infrastructure in Information Systems
• The Use of AI in Information Systems for Business Intelligence
• Information Systems in Healthcare: Improving Patient Care and Data Management
• The Influence of IoT on Information Systems Architecture
• Mobile Information Systems: Development and Usability Challenges
• The Role of Geographic Information Systems (GIS) in Urban Planning
• Social Media Analytics: Tools and Techniques in Information Systems
• Information Systems in Education: Enhancing Learning and Administration
• Cloud Computing Integration into Corporate Information Systems
• Information Systems Audit: Practices and Challenges
• User Interface Design and User Experience in Information Systems
• Privacy and Data Protection in Information Systems
• The Future of Quantum Computing in Information Systems
• The Role of Information Systems in Environmental Management
• Implementing Effective Knowledge Management Systems
• The Adoption of Virtual Reality in Information Systems
• The Challenges of Implementing ERP Systems in Multinational Corporations
• Information Systems for Real-Time Business Analytics
• The Impact of 5G Technology on Mobile Information Systems
• Ethical Issues in the Management of Information Systems
• Information Systems in Retail: Enhancing Customer Experience and Management
• The Role of Information Systems in Non-Profit Organizations
• Development of Decision Support Systems for Strategic Planning
• Information Systems in the Banking Sector: Enhancing Financial Services
• Risk Management in Information Systems
• The Integration of Artificial Neural Networks in Information Systems
• Information Systems and Corporate Governance
• Information Systems for Disaster Response and Management
• The Role of Information Systems in Sports Management
• Information Systems for Public Health Surveillance
• The Future of Information Systems: Trends and Predictions
• Information Systems in the Film and Media Industry
• Business Process Reengineering through Information Systems
• Implementing Customer Relationship Management (CRM) Systems in E-commerce
• Emerging Trends in Artificial Intelligence and Machine Learning
• The Future of Cloud Services and Technology
• Cybersecurity: Current Threats and Future Defenses
• The Role of Information Technology in Sustainable Energy Solutions
• Internet of Things (IoT): From Smart Homes to Smart Cities
• Blockchain and Its Impact on Information Technology
• The Use of Big Data Analytics in Predictive Modeling
• Virtual Reality (VR) and Augmented Reality (AR): The Next Frontier in IT
• The Challenges of Digital Transformation in Traditional Businesses
• Wearable Technology: Health Monitoring and Beyond
• 5G Technology: Implementation and Impacts on IT
• Biometrics Technology: Uses and Privacy Concerns
• The Role of IT in Global Health Initiatives
• Ethical Considerations in the Development of Autonomous Systems
• Data Privacy in the Age of Information Overload
• The Evolution of Software Development Methodologies
• Quantum Computing: The Next Revolution in IT
• IT Governance: Best Practices and Standards
• The Integration of AI in Customer Service Technology
• IT in Manufacturing: Industrial Automation and Robotics
• The Future of E-commerce: Technology and Trends
• Mobile Computing: Innovations and Challenges
• Information Technology in Education: Tools and Trends
• IT Project Management: Approaches and Tools
• The Role of IT in Media and Entertainment
• The Impact of Digital Marketing Technologies on Business Strategies
• IT in Logistics and Supply Chain Management
• The Development and Future of Autonomous Vehicles
• IT in the Insurance Sector: Enhancing Efficiency and Customer Engagement
• The Role of IT in Environmental Conservation
• Smart Grid Technology: IT at the Intersection of Energy Management
• Telemedicine: The Impact of IT on Healthcare Delivery
• IT in the Agricultural Sector: Innovations and Impact
• Cyber-Physical Systems: IT in the Integration of Physical and Digital Worlds
• The Influence of Social Media Platforms on IT Development
• Data Centers: Evolution, Technologies, and Sustainability
• IT in Public Administration: Improving Services and Transparency
• The Role of IT in Sports Analytics
• Information Technology in Retail: Enhancing the Shopping Experience
• The Future of IT: Integrating Ethical AI Systems

Internet of Things (IoT) Thesis Topics

• Enhancing IoT Security: Strategies for Safeguarding Connected Devices
• IoT in Smart Cities: Infrastructure and Data Management Challenges
• The Application of IoT in Precision Agriculture: Maximizing Efficiency and Yield
• IoT and Healthcare: Opportunities for Remote Monitoring and Patient Care
• Energy Efficiency in IoT: Techniques for Reducing Power Consumption in Devices
• The Role of IoT in Supply Chain Management and Logistics
• Real-Time Data Processing Using Edge Computing in IoT Networks
• Privacy Concerns and Data Protection in IoT Systems
• The Integration of IoT with Blockchain for Enhanced Security and Transparency
• IoT in Environmental Monitoring: Systems for Air Quality and Water Safety
• Predictive Maintenance in Industrial IoT: Strategies and Benefits
• IoT in Retail: Enhancing Customer Experience through Smart Technology
• The Development of Standard Protocols for IoT Communication
• IoT in Smart Homes: Automation and Security Systems
• The Role of IoT in Disaster Management: Early Warning Systems and Response Coordination
• Machine Learning Techniques for IoT Data Analytics
• IoT in Automotive: The Future of Connected and Autonomous Vehicles
• The Impact of 5G on IoT: Enhancements in Speed and Connectivity
• IoT Device Lifecycle Management: From Creation to Decommissioning
• IoT in Public Safety: Applications for Emergency Response and Crime Prevention
• The Ethics of IoT: Balancing Innovation with Consumer Rights
• IoT and the Future of Work: Automation and Labor Market Shifts
• Designing User-Friendly Interfaces for IoT Applications
• IoT in the Energy Sector: Smart Grids and Renewable Energy Integration
• Quantum Computing and IoT: Potential Impacts and Applications
• The Role of AI in Enhancing IoT Solutions
• IoT for Elderly Care: Technologies for Health and Mobility Assistance
• IoT in Education: Enhancing Classroom Experiences and Learning Outcomes
• Challenges in Scaling IoT Infrastructure for Global Coverage
• The Economic Impact of IoT: Industry Transformations and New Business Models
• IoT and Tourism: Enhancing Visitor Experiences through Connected Technologies
• Data Fusion Techniques in IoT: Integrating Diverse Data Sources
• IoT in Aquaculture: Monitoring and Managing Aquatic Environments
• Wireless Technologies for IoT: Comparing LoRa, Zigbee, and NB-IoT
• IoT and Intellectual Property: Navigating the Legal Landscape
• IoT in Sports: Enhancing Training and Audience Engagement
• Building Resilient IoT Systems against Cyber Attacks
• IoT for Waste Management: Innovations and System Implementations
• IoT in Agriculture: Drones and Sensors for Crop Monitoring
• The Role of IoT in Cultural Heritage Preservation: Monitoring and Maintenance
• Advanced Algorithms for Supervised and Unsupervised Learning
• Machine Learning in Genomics: Predicting Disease Propensity and Treatment Outcomes
• The Use of Neural Networks in Image Recognition and Analysis
• Reinforcement Learning: Applications in Robotics and Autonomous Systems
• The Role of Machine Learning in Natural Language Processing and Linguistic Analysis
• Deep Learning for Predictive Analytics in Business and Finance
• Machine Learning for Cybersecurity: Detection of Anomalies and Malware
• Ethical Considerations in Machine Learning: Bias and Fairness
• The Integration of Machine Learning with IoT for Smart Device Management
• Transfer Learning: Techniques and Applications in New Domains
• The Application of Machine Learning in Environmental Science
• Machine Learning in Healthcare: Diagnosing Conditions from Medical Images
• The Use of Machine Learning in Algorithmic Trading and Stock Market Analysis
• Machine Learning in Social Media: Sentiment Analysis and Trend Prediction
• Quantum Machine Learning: Merging Quantum Computing with AI
• Feature Engineering and Selection in Machine Learning
• Machine Learning for Enhancing User Experience in Mobile Applications
• The Impact of Machine Learning on Digital Marketing Strategies
• Machine Learning for Energy Consumption Forecasting and Optimization
• The Role of Machine Learning in Enhancing Network Security Protocols
• Scalability and Efficiency of Machine Learning Algorithms
• Machine Learning in Drug Discovery and Pharmaceutical Research
• The Application of Machine Learning in Sports Analytics
• Machine Learning for Real-Time Decision-Making in Autonomous Vehicles
• The Use of Machine Learning in Predicting Geographical and Meteorological Events
• Machine Learning for Educational Data Mining and Learning Analytics
• The Role of Machine Learning in Audio Signal Processing
• Predictive Maintenance in Manufacturing Through Machine Learning
• Machine Learning and Its Implications for Privacy and Surveillance
• The Application of Machine Learning in Augmented Reality Systems
• Deep Learning Techniques in Medical Diagnosis: Challenges and Opportunities
• The Use of Machine Learning in Video Game Development
• Machine Learning for Fraud Detection in Financial Services
• The Role of Machine Learning in Agricultural Optimization and Management
• The Impact of Machine Learning on Content Personalization and Recommendation Systems
• Machine Learning in Legal Tech: Document Analysis and Case Prediction
• Adaptive Learning Systems: Tailoring Education Through Machine Learning
• Machine Learning in Space Exploration: Analyzing Data from Space Missions
• Machine Learning for Public Sector Applications: Improving Services and Efficiency
• The Future of Machine Learning: Integrating Explainable AI
• Innovations in Convolutional Neural Networks for Image and Video Analysis
• Recurrent Neural Networks: Applications in Sequence Prediction and Analysis
• The Role of Neural Networks in Predicting Financial Market Trends
• Deep Neural Networks for Enhanced Speech Recognition Systems
• Neural Networks in Medical Imaging: From Detection to Diagnosis
• Generative Adversarial Networks (GANs): Applications in Art and Media
• The Use of Neural Networks in Autonomous Driving Technologies
• Neural Networks for Real-Time Language Translation
• The Application of Neural Networks in Robotics: Sensory Data and Movement Control
• Neural Network Optimization Techniques: Overcoming Overfitting and Underfitting
• The Integration of Neural Networks with Blockchain for Data Security
• Neural Networks in Climate Modeling and Weather Forecasting
• The Use of Neural Networks in Enhancing Internet of Things (IoT) Devices
• Graph Neural Networks: Applications in Social Network Analysis and Beyond
• The Impact of Neural Networks on Augmented Reality Experiences
• Neural Networks for Anomaly Detection in Network Security
• The Application of Neural Networks in Bioinformatics and Genomic Data Analysis
• Capsule Neural Networks: Improving the Robustness and Interpretability of Deep Learning
• The Role of Neural Networks in Consumer Behavior Analysis
• Neural Networks in Energy Sector: Forecasting and Optimization
• The Evolution of Neural Network Architectures for Efficient Learning
• The Use of Neural Networks in Sentiment Analysis: Techniques and Challenges
• Deep Reinforcement Learning: Strategies for Advanced Decision-Making Systems
• Neural Networks for Precision Medicine: Tailoring Treatments to Individual Genetic Profiles
• The Use of Neural Networks in Virtual Assistants: Enhancing Natural Language Understanding
• The Impact of Neural Networks on Pharmaceutical Research
• Neural Networks for Supply Chain Management: Prediction and Automation
• The Application of Neural Networks in E-commerce: Personalization and Recommendation Systems
• Neural Networks for Facial Recognition: Advances and Ethical Considerations
• The Role of Neural Networks in Educational Technologies
• The Use of Neural Networks in Predicting Economic Trends
• Neural Networks in Sports: Analyzing Performance and Strategy
• The Impact of Neural Networks on Digital Security Systems
• Neural Networks for Real-Time Video Surveillance Analysis
• The Integration of Neural Networks in Edge Computing Devices
• Neural Networks for Industrial Automation: Improving Efficiency and Accuracy
• The Future of Neural Networks: Towards More General AI Applications
• Neural Networks in Art and Design: Creating New Forms of Expression
• The Role of Neural Networks in Enhancing Public Health Initiatives
• The Future of Neural Networks: Challenges in Scalability and Generalization
• The Evolution of Programming Paradigms: Functional vs. Object-Oriented Programming
• Advances in Compiler Design and Optimization Techniques
• The Impact of Programming Languages on Software Security
• Developing Programming Languages for Quantum Computing
• Machine Learning in Automated Code Generation and Optimization
• The Role of Programming in Developing Scalable Cloud Applications
• The Future of Web Development: New Frameworks and Technologies
• Cross-Platform Development: Best Practices in Mobile App Programming
• The Influence of Programming Techniques on Big Data Analytics
• Real-Time Systems Programming: Challenges and Solutions
• The Integration of Programming with Blockchain Technology
• Programming for IoT: Languages and Tools for Device Communication
• Secure Coding Practices: Preventing Cyber Attacks through Software Design
• The Role of Programming in Data Visualization and User Interface Design
• Advances in Game Programming: Graphics, AI, and Network Play
• The Impact of Programming on Digital Media and Content Creation
• Programming Languages for Robotics: Trends and Future Directions
• The Use of Artificial Intelligence in Enhancing Programming Productivity
• Programming for Augmented and Virtual Reality: New Challenges and Techniques
• Ethical Considerations in Programming: Bias, Fairness, and Transparency
• The Future of Programming Education: Interactive and Adaptive Learning Models
• Programming for Wearable Technology: Special Considerations and Challenges
• The Evolution of Programming in Financial Technology
• Functional Programming in Enterprise Applications
• Memory Management Techniques in Programming: From Garbage Collection to Manual Control
• The Role of Open Source Programming in Accelerating Innovation
• The Impact of Programming on Network Security and Cryptography
• Developing Accessible Software: Programming for Users with Disabilities
• Programming Language Theories: New Models and Approaches
• The Challenges of Legacy Code: Strategies for Modernization and Integration
• Energy-Efficient Programming: Optimizing Code for Green Computing
• Multithreading and Concurrency: Advanced Programming Techniques
• The Impact of Programming on Computational Biology and Bioinformatics
• The Role of Scripting Languages in Automating System Administration
• Programming and the Future of Quantum Resistant Cryptography
• Code Review and Quality Assurance: Techniques and Tools
• Adaptive and Predictive Programming for Dynamic Environments
• The Role of Programming in Enhancing E-commerce Technology
• Programming for Cyber-Physical Systems: Bridging the Gap Between Digital and Physical
• The Influence of Programming Languages on Computational Efficiency and Performance
• Quantum Algorithms: Development and Applications Beyond Shor’s and Grover’s Algorithms
• The Role of Quantum Computing in Solving Complex Biological Problems
• Quantum Cryptography: New Paradigms for Secure Communication
• Error Correction Techniques in Quantum Computing
• Quantum Computing and Its Impact on Artificial Intelligence
• The Integration of Classical and Quantum Computing: Hybrid Models
• Quantum Machine Learning: Theoretical Foundations and Practical Applications
• Quantum Computing Hardware: Advances in Qubit Technology
• The Application of Quantum Computing in Financial Modeling and Risk Assessment
• Quantum Networking: Establishing Secure Quantum Communication Channels
• The Future of Drug Discovery: Applications of Quantum Computing
• Quantum Computing in Cryptanalysis: Threats to Current Cryptography Standards
• Simulation of Quantum Systems for Material Science
• Quantum Computing for Optimization Problems in Logistics and Manufacturing
• Theoretical Limits of Quantum Computing: Understanding Quantum Complexity
• Quantum Computing and the Future of Search Algorithms
• The Role of Quantum Computing in Climate Science and Environmental Modeling
• Quantum Annealing vs. Universal Quantum Computing: Comparative Studies
• Implementing Quantum Algorithms in Quantum Programming Languages
• The Impact of Quantum Computing on Public Key Cryptography
• Quantum Entanglement: Experiments and Applications in Quantum Networks
• Scalability Challenges in Quantum Processors
• The Ethics and Policy Implications of Quantum Computing
• Quantum Computing in Space Exploration and Astrophysics
• The Role of Quantum Computing in Developing Next-Generation AI Systems
• Quantum Computing in the Energy Sector: Applications in Smart Grids and Nuclear Fusion
• Noise and Decoherence in Quantum Computers: Overcoming Practical Challenges
• Quantum Computing for Predicting Economic Market Trends
• Quantum Sensors: Enhancing Precision in Measurement and Imaging
• The Future of Quantum Computing Education and Workforce Development
• Quantum Computing in Cybersecurity: Preparing for a Post-Quantum World
• Quantum Computing and the Internet of Things: Potential Intersections
• Practical Quantum Computing: From Theory to Real-World Applications
• Quantum Supremacy: Milestones and Future Goals
• The Role of Quantum Computing in Genetics and Genomics
• Quantum Computing for Material Discovery and Design
• The Challenges of Quantum Programming Languages and Environments
• Quantum Computing in Art and Creative Industries
• The Global Race for Quantum Computing Supremacy: Technological and Political Aspects
• Quantum Computing and Its Implications for Software Engineering
• Advances in Humanoid Robotics: New Developments and Challenges
• Robotics in Healthcare: From Surgery to Rehabilitation
• The Integration of AI in Robotics: Enhanced Autonomy and Learning Capabilities
• Swarm Robotics: Coordination Strategies and Applications
• The Use of Robotics in Hazardous Environments: Deep Sea and Space Exploration
• Soft Robotics: Materials, Design, and Applications
• Robotics in Agriculture: Automation of Farming and Harvesting Processes
• The Role of Robotics in Manufacturing: Increased Efficiency and Flexibility
• Ethical Considerations in the Deployment of Robots in Human Environments
• Autonomous Vehicles: Technological Advances and Regulatory Challenges
• Robotic Assistants for the Elderly and Disabled: Improving Quality of Life
• The Use of Robotics in Education: Teaching Science, Technology, Engineering, and Math (STEM)
• Robotics and Computer Vision: Enhancing Perception and Decision Making
• The Impact of Robotics on Employment and the Workforce
• The Development of Robotic Systems for Environmental Monitoring and Conservation
• Machine Learning Techniques for Robotic Perception and Navigation
• Advances in Robotic Surgery: Precision and Outcomes
• Human-Robot Interaction: Building Trust and Cooperation
• Robotics in Retail: Automated Warehousing and Customer Service
• Energy-Efficient Robots: Design and Utilization
• Robotics in Construction: Automation and Safety Improvements
• The Role of Robotics in Disaster Response and Recovery Operations
• The Application of Robotics in Art and Creative Industries
• Robotics and the Future of Personal Transportation
• Ethical AI in Robotics: Ensuring Safe and Fair Decision-Making
• The Use of Robotics in Logistics: Drones and Autonomous Delivery Vehicles
• Robotics in the Food Industry: From Production to Service
• The Integration of IoT with Robotics for Enhanced Connectivity
• Wearable Robotics: Exoskeletons for Rehabilitation and Enhanced Mobility
• The Impact of Robotics on Privacy and Security
• Robotic Pet Companions: Social Robots and Their Psychological Effects
• Robotics for Planetary Exploration and Colonization
• Underwater Robotics: Innovations in Oceanography and Marine Biology
• Advances in Robotics Programming Languages and Tools
• The Role of Robotics in Minimizing Human Exposure to Contaminants and Pathogens
• Collaborative Robots (Cobots): Working Alongside Humans in Shared Spaces
• The Use of Robotics in Entertainment and Sports
• Robotics and Machine Ethics: Programming Moral Decision-Making
• The Future of Military Robotics: Opportunities and Challenges
• Sustainable Robotics: Reducing the Environmental Impact of Robotic Systems
• Agile Methodologies: Evolution and Future Trends
• DevOps Practices: Improving Software Delivery and Lifecycle Management
• The Impact of Microservices Architecture on Software Development
• Containerization Technologies: Docker, Kubernetes, and Beyond
• Software Quality Assurance: Modern Techniques and Tools
• The Role of Artificial Intelligence in Automated Software Testing
• Blockchain Applications in Software Development and Security
• The Integration of Continuous Integration and Continuous Deployment (CI/CD) in Software Projects
• Cybersecurity in Software Engineering: Best Practices for Secure Coding
• Low-Code and No-Code Development: Implications for Professional Software Development
• The Future of Software Engineering Education
• Software Sustainability: Developing Green Software and Reducing Carbon Footprints
• The Role of Software Engineering in Healthcare: Telemedicine and Patient Data Management
• Privacy by Design: Incorporating Privacy Features at the Development Stage
• The Impact of Quantum Computing on Software Engineering
• Software Engineering for Augmented and Virtual Reality: Challenges and Innovations
• Cloud-Native Applications: Design, Development, and Deployment
• Software Project Management: Agile vs. Traditional Approaches
• Open Source Software: Community Engagement and Project Sustainability
• The Evolution of Graphical User Interfaces in Application Development
• The Challenges of Integrating IoT Devices into Software Systems
• Ethical Issues in Software Engineering: Bias, Accountability, and Regulation
• Software Engineering for Autonomous Vehicles: Safety and Regulatory Considerations
• Big Data Analytics in Software Development: Enhancing Decision-Making Processes
• The Future of Mobile App Development: Trends and Technologies
• The Role of Software Engineering in Artificial Intelligence: Frameworks and Algorithms
• Performance Optimization in Software Applications
• Adaptive Software Development: Responding to Changing User Needs
• Software Engineering in Financial Services: Compliance and Security Challenges
• User Experience (UX) Design in Software Engineering
• The Role of Software Engineering in Smart Cities: Infrastructure and Services
• The Impact of 5G on Software Development and Deployment
• Real-Time Systems in Software Engineering: Design and Implementation Challenges
• Cross-Platform Development Challenges: Ensuring Consistency and Performance
• Software Testing Automation: Tools and Trends
• The Integration of Cyber-Physical Systems in Software Engineering
• Software Engineering in the Entertainment Industry: Game Development and Beyond
• The Application of Machine Learning in Predicting Software Bugs
• The Role of Software Engineering in Cybersecurity Defense Strategies
• Accessibility in Software Engineering: Creating Inclusive and Usable Software
• Progressive Web Apps (PWAs): Advantages and Implementation Challenges
• The Future of Web Accessibility: Standards and Practices
• Single-Page Applications (SPAs) vs. Multi-Page Applications (MPAs): Performance and Usability
• The Impact of Serverless Computing on Web Development
• The Evolution of CSS for Modern Web Design
• Security Best Practices in Web Development: Defending Against XSS and CSRF Attacks
• The Role of Web Development in Enhancing E-commerce User Experience
• The Use of Artificial Intelligence in Web Personalization and User Engagement
• The Future of Web APIs: Standards, Security, and Scalability
• Responsive Web Design: Techniques and Trends
• JavaScript Frameworks: Vue.js, React.js, and Angular – A Comparative Analysis
• Web Development for IoT: Interfaces and Connectivity Solutions
• The Impact of 5G on Web Development and User Experiences
• The Use of Blockchain Technology in Web Development for Enhanced Security
• Web Development in the Cloud: Using AWS, Azure, and Google Cloud
• Content Management Systems (CMS): Trends and Future Developments
• The Application of Web Development in Virtual and Augmented Reality
• The Importance of Web Performance Optimization: Tools and Techniques
• Sustainable Web Design: Practices for Reducing Energy Consumption
• The Role of Web Development in Digital Marketing: SEO and Social Media Integration
• Headless CMS: Benefits and Challenges for Developers and Content Creators
• The Future of Web Typography: Design, Accessibility, and Performance
• Web Development and Data Protection: Complying with GDPR and Other Regulations
• Real-Time Web Communication: Technologies like WebSockets and WebRTC
• Front-End Development Tools: Efficiency and Innovation in Workflow
• The Challenges of Migrating Legacy Systems to Modern Web Architectures
• Microfrontends Architecture: Designing Scalable and Decoupled Web Applications
• The Impact of Cryptocurrencies on Web Payment Systems
• User-Centered Design in Web Development: Methods for Engaging Users
• The Role of Web Development in Business Intelligence: Dashboards and Reporting Tools
• Web Development for Mobile Platforms: Optimization and Best Practices
• The Evolution of E-commerce Platforms: From Web to Mobile Commerce
• Web Security in E-commerce: Protecting Transactions and User Data
• Dynamic Web Content: Server-Side vs. Client-Side Rendering
• The Future of Full Stack Development: Trends and Skills
• Web Design Psychology: How Design Influences User Behavior
• The Role of Web Development in the Non-Profit Sector: Fundraising and Community Engagement
• The Integration of AI Chatbots in Web Development
• The Use of Motion UI in Web Design: Enhancing Aesthetics and User Interaction
• The Future of Web Development: Predictions and Emerging Technologies

We trust that this comprehensive list of computer science thesis topics will serve as a valuable starting point for your research endeavors. With 1000 unique and carefully selected topics distributed across 25 key areas of computer science, students are equipped to tackle complex questions and contribute meaningful advancements to the field. As you proceed to select your thesis topic, consider not only your personal interests and career goals but also the potential impact of your research. We encourage you to explore these topics thoroughly and choose one that will not only challenge you but also push the boundaries of technology and innovation.

The Range of Computer Science Thesis Topics

Computer science stands as a dynamic and ever-evolving field that continuously reshapes how we interact with the world. At its core, the discipline encompasses not just the study of algorithms and computation, but a broad spectrum of practical and theoretical knowledge areas that drive innovation in various sectors. This article aims to explore the rich landscape of computer science thesis topics, offering students and researchers a glimpse into the potential areas of study that not only challenge the intellect but also contribute significantly to technological progress. As we delve into the current issues, recent trends, and future directions of computer science, it becomes evident that the possibilities for research are both vast and diverse. Whether you are intrigued by the complexities of artificial intelligence, the robust architecture of networks and systems, or the innovative approaches in cybersecurity, computer science offers a fertile ground for developing thesis topics that are as impactful as they are intellectually stimulating.

Current Issues in Computer Science

One of the prominent current issues in computer science revolves around data security and privacy. As digital transformation accelerates across industries, the massive influx of data generated poses significant challenges in terms of its protection and ethical use. Cybersecurity threats have become more sophisticated, with data breaches and cyber-attacks causing major concerns for organizations worldwide. This ongoing battle demands continuous improvements in security protocols and the development of robust cybersecurity measures. Computer science thesis topics in this area can explore new cryptographic methods, intrusion detection systems, and secure communication protocols to fortify digital defenses. Research could also delve into the ethical implications of data collection and use, proposing frameworks that ensure privacy while still leveraging data for innovation.

Another critical issue facing the field of computer science is the ethical development and deployment of artificial intelligence (AI) systems. As AI technologies become more integrated into daily life and critical infrastructure, concerns about bias, fairness, and accountability in AI systems have intensified. Thesis topics could focus on developing algorithms that address these ethical concerns, including techniques for reducing bias in machine learning models and methods for increasing transparency and explainability in AI decisions. This research is crucial for ensuring that AI technologies promote fairness and do not perpetuate or exacerbate existing societal inequalities.

Furthermore, the rapid pace of technological change presents a challenge in terms of sustainability and environmental impact. The energy consumption of large data centers, the carbon footprint of producing and disposing of electronic waste, and the broader effects of high-tech innovations on the environment are significant concerns within computer science. Thesis research in this domain could focus on creating more energy-efficient computing methods, developing algorithms that reduce power consumption, or innovating recycling technologies that address the issue of e-waste. This research not only contributes to the field of computer science but also plays a crucial role in ensuring that technological advancement does not come at an unsustainable cost to the environment.

These current issues highlight the dynamic nature of computer science and its direct impact on society. Addressing these challenges through focused research and innovative thesis topics not only advances the field but also contributes to resolving some of the most pressing problems facing our global community today.

Recent Trends in Computer Science

In recent years, computer science has witnessed significant advancements in the integration of artificial intelligence (AI) and machine learning (ML) across various sectors, marking one of the most exciting trends in the field. These technologies are not just reshaping traditional industries but are also at the forefront of driving innovations in areas like healthcare, finance, and autonomous systems. Thesis topics within this trend could explore the development of advanced ML algorithms that enhance predictive analytics, improve automated decision-making, or refine natural language processing capabilities. Additionally, AI’s role in ethical decision-making and its societal impacts offers a rich vein of inquiry for research, focusing on mitigating biases and ensuring that AI systems operate transparently and justly.

Another prominent trend in computer science is the rapid growth of blockchain technology beyond its initial application in cryptocurrencies. Blockchain is proving its potential in creating more secure, decentralized, and transparent networks for a variety of applications, from enhancing supply chain logistics to revolutionizing digital identity verification processes. Computer science thesis topics could investigate novel uses of blockchain for ensuring data integrity in digital transactions, enhancing cybersecurity measures, or even developing new frameworks for blockchain integration into existing technological infrastructures. The exploration of blockchain’s scalability, speed, and energy consumption also presents critical research opportunities that are timely and relevant.

Furthermore, the expansion of the Internet of Things (IoT) continues to be a significant trend, with more devices becoming connected every day, leading to increasingly smart environments. This proliferation poses unique challenges and opportunities for computer science research, particularly in terms of scalability, security, and new data management strategies. Thesis topics might focus on optimizing network protocols to handle the massive influx of data from IoT devices, developing solutions to safeguard against IoT-specific security vulnerabilities, or innovative applications of IoT in urban planning, smart homes, or healthcare. Research in this area is crucial for advancing the efficiency and functionality of IoT systems and for ensuring they can be safely and effectively integrated into modern life.

These recent trends underscore the vibrant and ever-evolving nature of computer science, reflecting its capacity to influence and transform an array of sectors through technological innovation. The continual emergence of new research topics within these trends not only enriches the academic discipline but also provides substantial benefits to society by addressing practical challenges and enhancing the capabilities of technology in everyday life.

Future Directions in Computer Science

As we look toward the future, one of the most anticipated areas in computer science is the advancement of quantum computing. This emerging technology promises to revolutionize problem-solving in fields that require immense computational power, such as cryptography, drug discovery, and complex system modeling. Quantum computing has the potential to process tasks at speeds unachievable by classical computers, offering breakthroughs in materials science and encryption methods. Computer science thesis topics might explore the theoretical underpinnings of quantum algorithms, the development of quantum-resistant cryptographic systems, or practical applications of quantum computing in industry-specific scenarios. Research in this area not only contributes to the foundational knowledge of quantum mechanics but also paves the way for its integration into mainstream computing, marking a significant leap forward in computational capabilities.

Another promising direction in computer science is the advancement of autonomous systems, particularly in robotics and vehicle automation. The future of autonomous technologies hinges on improving their safety, reliability, and decision-making processes under uncertain conditions. Thesis topics could focus on the enhancement of machine perception through computer vision and sensor fusion, the development of more sophisticated AI-driven decision frameworks, or ethical considerations in the deployment of autonomous systems. As these technologies become increasingly prevalent, research will play a crucial role in addressing the societal and technical challenges they present, ensuring their beneficial integration into daily life and industry operations.

Additionally, the ongoing expansion of artificial intelligence applications poses significant future directions for research, especially in the realm of AI ethics and policy. As AI systems become more capable and widespread, their impact on privacy, employment, and societal norms continues to grow. Future thesis topics might delve into the development of guidelines and frameworks for responsible AI, studies on the impact of AI on workforce dynamics, or innovations in transparent and fair AI systems. This research is vital for guiding the ethical evolution of AI technologies, ensuring they enhance societal well-being without diminishing human dignity or autonomy.

These future directions in computer science not only highlight the field’s potential for substantial technological advancements but also underscore the importance of thoughtful consideration of their broader implications. By exploring these areas in depth, computer science research can lead the way in not just technological innovation, but also in shaping a future where technology and ethics coexist harmoniously for the betterment of society.

In conclusion, the field of computer science is not only foundational to the technological advancements that characterize the modern age but also crucial in solving some of the most pressing challenges of our time. The potential thesis topics discussed in this article reflect a mere fraction of the opportunities that lie in the realms of theory, application, and innovation within this expansive field. As emerging technologies such as quantum computing, artificial intelligence, and blockchain continue to evolve, they open new avenues for research that could potentially redefine existing paradigms. For students embarking on their thesis journey, it is essential to choose a topic that not only aligns with their academic passions but also contributes to the ongoing expansion of computer science knowledge. By pushing the boundaries of what is known and exploring uncharted territories, students can leave a lasting impact on the field and pave the way for future technological breakthroughs. As we look forward, it’s clear that computer science will continue to be a key driver of change, making it an exciting and rewarding area for academic and professional growth.

Thesis Writing Services by iResearchNet

At iResearchNet, we specialize in providing exceptional thesis writing services tailored to meet the diverse needs of students, particularly those pursuing advanced topics in computer science. Understanding the pivotal role a thesis plays in a student’s academic career, we offer a suite of services designed to assist students in crafting papers that are not only well-researched and insightful but also perfectly aligned with their academic objectives. Here are the key features of our thesis writing services:

• Expert Degree-Holding Writers : Our team consists of writers who hold advanced degrees in computer science and related fields. Their academic and professional backgrounds ensure that they bring a wealth of knowledge and expertise to your thesis.
• Custom Written Works : Every thesis we produce is tailor-made to meet the specific requirements and guidelines provided by the student. This bespoke approach ensures that each paper is unique and of the highest quality.
• In-depth Research : We pride ourselves on conducting thorough and comprehensive research for every thesis. Our writers utilize the latest resources, databases, and scholarly articles to gather the most relevant and up-to-date information.
• Custom Formatting : Each thesis is formatted according to academic standards and the specific requirements of the student’s program, whether it’s APA, MLA, Chicago/Turabian, or Harvard style.
• Top Quality : Quality is at the core of our services. From language clarity to factual accuracy, each thesis is crafted to meet the highest academic standards.
• Customized Solutions : Recognizing that every student’s needs are different, we offer customized solutions that cater to individual preferences and requirements.
• Flexible Pricing : We provide a range of pricing options to accommodate students’ different budgets, ensuring that our services are accessible to everyone.
• Short Deadlines : Our services are designed to accommodate even the tightest deadlines, with the ability to handle requests that require a turnaround as quick as 3 hours.
• Timely Delivery : We guarantee timely delivery of all our papers, helping students meet their submission deadlines without compromising on quality.
• 24/7 Support : Our customer support team is available around the clock to answer any questions and provide assistance whenever needed.
• Absolute Privacy : We maintain a strict privacy policy to ensure that all client information is kept confidential and secure.
• Easy Order Tracking : Our client portal allows for easy tracking of orders, giving students the ability to monitor the progress of their thesis writing process.
• Money-Back Guarantee : We offer a money-back guarantee to ensure that all students are completely satisfied with our services.

At iResearchNet, we are dedicated to supporting students by providing them with high-quality, reliable, and professional thesis writing services. By choosing us, students can be confident that they are receiving expert help that not only meets but exceeds their expectations. Whether you are tackling complex topics in computer science or any other academic discipline, our team is here to help you achieve academic success.

Order Your Custom Thesis Paper Today!

Are you ready to take the next step towards academic excellence in computer science? At iResearchNet, we are committed to helping you achieve your academic goals with our premier thesis writing services. Our team of expert writers is equipped to handle the most challenging topics and tightest deadlines, ensuring that you receive a top-quality, custom-written thesis that not only meets but exceeds your academic requirements.

Don’t let the stress of thesis writing hold you back. Whether you’re grappling with complex algorithms, innovative software solutions, or groundbreaking data analysis, our custom thesis papers are crafted to provide you with the insights and depth needed to excel. With flexible pricing, personalized support, and guaranteed confidentiality, you can trust iResearchNet to be your partner in your academic journey.

Act now to secure your future! Visit our website to place your order or speak with one of our representatives to learn more about how we can assist you. Remember, when you choose iResearchNet, you’re not just getting a thesis paper; you’re investing in your success. Order your custom thesis paper today and take the first step towards standing out in the competitive field of computer science. With iResearchNet, you’re one step closer to not only completing your degree but also making a significant impact in the world of technology.

ORDER HIGH QUALITY CUSTOM PAPER

Research Topics & Ideas: Education

170+ Research Ideas To Fast-Track Your Project

If you’re just starting out exploring education-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from actual dissertations and theses..

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Education Research Topics

• How to find a research topic (video)
• List of 50+ education-related research topics/ideas
• List of 120+ level-specific research topics 
• Examples of actual dissertation topics in education
• Tips to fast-track your topic ideation (video)
• Free Webinar : Topic Ideation 101
• Where to get extra help

Education-Related Research Topics & Ideas

Below you’ll find a list of education-related research topics and idea kickstarters. These are fairly broad and flexible to various contexts, so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• The impact of school funding on student achievement
• The effects of social and emotional learning on student well-being
• The effects of parental involvement on student behaviour
• The impact of teacher training on student learning
• The impact of classroom design on student learning
• The impact of poverty on education
• The use of student data to inform instruction
• The role of parental involvement in education
• The effects of mindfulness practices in the classroom
• The use of technology in the classroom
• The role of critical thinking in education
• The use of formative and summative assessments in the classroom
• The use of differentiated instruction in the classroom
• The use of gamification in education
• The effects of teacher burnout on student learning
• The impact of school leadership on student achievement
• The effects of teacher diversity on student outcomes
• The role of teacher collaboration in improving student outcomes
• The implementation of blended and online learning
• The effects of teacher accountability on student achievement
• The effects of standardized testing on student learning
• The effects of classroom management on student behaviour
• The effects of school culture on student achievement
• The use of student-centred learning in the classroom
• The impact of teacher-student relationships on student outcomes
• The achievement gap in minority and low-income students
• The use of culturally responsive teaching in the classroom
• The impact of teacher professional development on student learning
• The use of project-based learning in the classroom
• The effects of teacher expectations on student achievement
• The use of adaptive learning technology in the classroom
• The impact of teacher turnover on student learning
• The effects of teacher recruitment and retention on student learning
• The impact of early childhood education on later academic success
• The impact of parental involvement on student engagement
• The use of positive reinforcement in education
• The impact of school climate on student engagement
• The role of STEM education in preparing students for the workforce
• The effects of school choice on student achievement
• The use of technology in the form of online tutoring

Level-Specific Research Topics

Looking for research topics for a specific level of education? We’ve got you covered. Below you can find research topic ideas for primary, secondary and tertiary-level education contexts. Click the relevant level to view the respective list.

Research Topics: Pick An Education Level

Primary education.

• Investigating the effects of peer tutoring on academic achievement in primary school
• Exploring the benefits of mindfulness practices in primary school classrooms
• Examining the effects of different teaching strategies on primary school students’ problem-solving skills
• The use of storytelling as a teaching strategy in primary school literacy instruction
• The role of cultural diversity in promoting tolerance and understanding in primary schools
• The impact of character education programs on moral development in primary school students
• Investigating the use of technology in enhancing primary school mathematics education
• The impact of inclusive curriculum on promoting equity and diversity in primary schools
• The impact of outdoor education programs on environmental awareness in primary school students
• The influence of school climate on student motivation and engagement in primary schools
• Investigating the effects of early literacy interventions on reading comprehension in primary school students
• The impact of parental involvement in school decision-making processes on student achievement in primary schools
• Exploring the benefits of inclusive education for students with special needs in primary schools
• Investigating the effects of teacher-student feedback on academic motivation in primary schools
• The role of technology in developing digital literacy skills in primary school students
• Effective strategies for fostering a growth mindset in primary school students
• Investigating the role of parental support in reducing academic stress in primary school children
• The role of arts education in fostering creativity and self-expression in primary school students
• Examining the effects of early childhood education programs on primary school readiness
• Examining the effects of homework on primary school students’ academic performance
• The role of formative assessment in improving learning outcomes in primary school classrooms
• The impact of teacher-student relationships on academic outcomes in primary school
• Investigating the effects of classroom environment on student behavior and learning outcomes in primary schools
• Investigating the role of creativity and imagination in primary school curriculum
• The impact of nutrition and healthy eating programs on academic performance in primary schools
• The impact of social-emotional learning programs on primary school students’ well-being and academic performance
• The role of parental involvement in academic achievement of primary school children
• Examining the effects of classroom management strategies on student behavior in primary school
• The role of school leadership in creating a positive school climate Exploring the benefits of bilingual education in primary schools
• The effectiveness of project-based learning in developing critical thinking skills in primary school students
• The role of inquiry-based learning in fostering curiosity and critical thinking in primary school students
• The effects of class size on student engagement and achievement in primary schools
• Investigating the effects of recess and physical activity breaks on attention and learning in primary school
• Exploring the benefits of outdoor play in developing gross motor skills in primary school children
• The effects of educational field trips on knowledge retention in primary school students
• Examining the effects of inclusive classroom practices on students’ attitudes towards diversity in primary schools
• The impact of parental involvement in homework on primary school students’ academic achievement
• Investigating the effectiveness of different assessment methods in primary school classrooms
• The influence of physical activity and exercise on cognitive development in primary school children
• Exploring the benefits of cooperative learning in promoting social skills in primary school students

Secondary Education

• Investigating the effects of school discipline policies on student behavior and academic success in secondary education
• The role of social media in enhancing communication and collaboration among secondary school students
• The impact of school leadership on teacher effectiveness and student outcomes in secondary schools
• Investigating the effects of technology integration on teaching and learning in secondary education
• Exploring the benefits of interdisciplinary instruction in promoting critical thinking skills in secondary schools
• The impact of arts education on creativity and self-expression in secondary school students
• The effectiveness of flipped classrooms in promoting student learning in secondary education
• The role of career guidance programs in preparing secondary school students for future employment
• Investigating the effects of student-centered learning approaches on student autonomy and academic success in secondary schools
• The impact of socio-economic factors on educational attainment in secondary education
• Investigating the impact of project-based learning on student engagement and academic achievement in secondary schools
• Investigating the effects of multicultural education on cultural understanding and tolerance in secondary schools
• The influence of standardized testing on teaching practices and student learning in secondary education
• Investigating the effects of classroom management strategies on student behavior and academic engagement in secondary education
• The influence of teacher professional development on instructional practices and student outcomes in secondary schools
• The role of extracurricular activities in promoting holistic development and well-roundedness in secondary school students
• Investigating the effects of blended learning models on student engagement and achievement in secondary education
• The role of physical education in promoting physical health and well-being among secondary school students
• Investigating the effects of gender on academic achievement and career aspirations in secondary education
• Exploring the benefits of multicultural literature in promoting cultural awareness and empathy among secondary school students
• The impact of school counseling services on student mental health and well-being in secondary schools
• Exploring the benefits of vocational education and training in preparing secondary school students for the workforce
• The role of digital literacy in preparing secondary school students for the digital age
• The influence of parental involvement on academic success and well-being of secondary school students
• The impact of social-emotional learning programs on secondary school students’ well-being and academic success
• The role of character education in fostering ethical and responsible behavior in secondary school students
• Examining the effects of digital citizenship education on responsible and ethical technology use among secondary school students
• The impact of parental involvement in school decision-making processes on student outcomes in secondary schools
• The role of educational technology in promoting personalized learning experiences in secondary schools
• The impact of inclusive education on the social and academic outcomes of students with disabilities in secondary schools
• The influence of parental support on academic motivation and achievement in secondary education
• The role of school climate in promoting positive behavior and well-being among secondary school students
• Examining the effects of peer mentoring programs on academic achievement and social-emotional development in secondary schools
• Examining the effects of teacher-student relationships on student motivation and achievement in secondary schools
• Exploring the benefits of service-learning programs in promoting civic engagement among secondary school students
• The impact of educational policies on educational equity and access in secondary education
• Examining the effects of homework on academic achievement and student well-being in secondary education
• Investigating the effects of different assessment methods on student performance in secondary schools
• Examining the effects of single-sex education on academic performance and gender stereotypes in secondary schools
• The role of mentoring programs in supporting the transition from secondary to post-secondary education

Tertiary Education

• The role of student support services in promoting academic success and well-being in higher education
• The impact of internationalization initiatives on students’ intercultural competence and global perspectives in tertiary education
• Investigating the effects of active learning classrooms and learning spaces on student engagement and learning outcomes in tertiary education
• Exploring the benefits of service-learning experiences in fostering civic engagement and social responsibility in higher education
• The influence of learning communities and collaborative learning environments on student academic and social integration in higher education
• Exploring the benefits of undergraduate research experiences in fostering critical thinking and scientific inquiry skills
• Investigating the effects of academic advising and mentoring on student retention and degree completion in higher education
• The role of student engagement and involvement in co-curricular activities on holistic student development in higher education
• The impact of multicultural education on fostering cultural competence and diversity appreciation in higher education
• The role of internships and work-integrated learning experiences in enhancing students’ employability and career outcomes
• Examining the effects of assessment and feedback practices on student learning and academic achievement in tertiary education
• The influence of faculty professional development on instructional practices and student outcomes in tertiary education
• The influence of faculty-student relationships on student success and well-being in tertiary education
• The impact of college transition programs on students’ academic and social adjustment to higher education
• The impact of online learning platforms on student learning outcomes in higher education
• The impact of financial aid and scholarships on access and persistence in higher education
• The influence of student leadership and involvement in extracurricular activities on personal development and campus engagement
• Exploring the benefits of competency-based education in developing job-specific skills in tertiary students
• Examining the effects of flipped classroom models on student learning and retention in higher education
• Exploring the benefits of online collaboration and virtual team projects in developing teamwork skills in tertiary students
• Investigating the effects of diversity and inclusion initiatives on campus climate and student experiences in tertiary education
• The influence of study abroad programs on intercultural competence and global perspectives of college students
• Investigating the effects of peer mentoring and tutoring programs on student retention and academic performance in tertiary education
• Investigating the effectiveness of active learning strategies in promoting student engagement and achievement in tertiary education
• Investigating the effects of blended learning models and hybrid courses on student learning and satisfaction in higher education
• The role of digital literacy and information literacy skills in supporting student success in the digital age
• Investigating the effects of experiential learning opportunities on career readiness and employability of college students
• The impact of e-portfolios on student reflection, self-assessment, and showcasing of learning in higher education
• The role of technology in enhancing collaborative learning experiences in tertiary classrooms
• The impact of research opportunities on undergraduate student engagement and pursuit of advanced degrees
• Examining the effects of competency-based assessment on measuring student learning and achievement in tertiary education
• Examining the effects of interdisciplinary programs and courses on critical thinking and problem-solving skills in college students
• The role of inclusive education and accessibility in promoting equitable learning experiences for diverse student populations
• The role of career counseling and guidance in supporting students’ career decision-making in tertiary education
• The influence of faculty diversity and representation on student success and inclusive learning environments in higher education

Education-Related Dissertations & Theses

While the ideas we’ve presented above are a decent starting point for finding a research topic in education, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses in the education space to see how this all comes together in practice.

Below, we’ve included a selection of education-related research projects to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• From Rural to Urban: Education Conditions of Migrant Children in China (Wang, 2019)
• Energy Renovation While Learning English: A Guidebook for Elementary ESL Teachers (Yang, 2019)
• A Reanalyses of Intercorrelational Matrices of Visual and Verbal Learners’ Abilities, Cognitive Styles, and Learning Preferences (Fox, 2020)
• A study of the elementary math program utilized by a mid-Missouri school district (Barabas, 2020)
• Instructor formative assessment practices in virtual learning environments : a posthumanist sociomaterial perspective (Burcks, 2019)
• Higher education students services: a qualitative study of two mid-size universities’ direct exchange programs (Kinde, 2020)
• Exploring editorial leadership : a qualitative study of scholastic journalism advisers teaching leadership in Missouri secondary schools (Lewis, 2020)
• Selling the virtual university: a multimodal discourse analysis of marketing for online learning (Ludwig, 2020)
• Advocacy and accountability in school counselling: assessing the use of data as related to professional self-efficacy (Matthews, 2020)
• The use of an application screening assessment as a predictor of teaching retention at a midwestern, K-12, public school district (Scarbrough, 2020)
• Core values driving sustained elite performance cultures (Beiner, 2020)
• Educative features of upper elementary Eureka math curriculum (Dwiggins, 2020)
• How female principals nurture adult learning opportunities in successful high schools with challenging student demographics (Woodward, 2020)
• The disproportionality of Black Males in Special Education: A Case Study Analysis of Educator Perceptions in a Southeastern Urban High School (McCrae, 2021)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic within education, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

You Might Also Like:

66 Comments

This is an helpful tool 🙏

Special education

Really appreciated by this . It is the best platform for research related items

Research title related to school of students

How are you

I think this platform is actually good enough.

Research title related to students

My field is research measurement and evaluation. Need dissertation topics in the field

Assalam o Alaikum I’m a student Bs educational Resarch and evaluation I’m confused to choose My thesis title please help me in choose the thesis title

Good idea I’m going to teach my colleagues

You can find our list of nursing-related research topic ideas here: https://gradcoach.com/research-topics-nursing/

Write on action research topic, using guidance and counseling to address unwanted teenage pregnancy in school

Thanks a lot

I learned a lot from this site, thank you so much!

Thank you for the information.. I would like to request a topic based on school major in social studies

parental involvement and students academic performance

Science education topics?

plz tell me if you got some good topics, im here for finding research topic for masters degree

How about School management and supervision pls.?

Hi i am an Deputy Principal in a primary school. My wish is to srudy foe Master’s degree in Education.Please advice me on which topic can be relevant for me. Thanks.

Every topic proposed above on primary education is a starting point for me. I appreciate immensely the team that has sat down to make a detail of these selected topics just for beginners like us. Be blessed.

Kindly help me with the research questions on the topic” Effects of workplace conflict on the employees’ job performance”. The effects can be applicable in every institution,enterprise or organisation.

Greetings, I am a student majoring in Sociology and minoring in Public Administration. I’m considering any recommended research topic in the field of Sociology.

I’m a student pursuing Mphil in Basic education and I’m considering any recommended research proposal topic in my field of study

Research Defense for students in senior high

Kindly help me with a research topic in educational psychology. Ph.D level. Thank you.

Project-based learning is a teaching/learning type,if well applied in a classroom setting will yield serious positive impact. What can a teacher do to implement this in a disadvantaged zone like “North West Region of Cameroon ( hinterland) where war has brought about prolonged and untold sufferings on the indegins?

I wish to get help on topics of research on educational administration

I wish to get help on topics of research on educational administration PhD level

I am also looking for such type of title

I am a student of undergraduate, doing research on how to use guidance and counseling to address unwanted teenage pregnancy in school

the topics are very good regarding research & education .

Can i request your suggestion topic for my Thesis about Teachers as an OFW. thanx you

Would like to request for suggestions on a topic in Economics of education,PhD level

Would like to request for suggestions on a topic in Economics of education

Hi 👋 I request that you help me with a written research proposal about education the format

Am offering degree in education senior high School Accounting. I want a topic for my project work

l would like to request suggestions on a topic in managing teaching and learning, PhD level (educational leadership and management)

request suggestions on a topic in managing teaching and learning, PhD level (educational leadership and management)

I would to inquire on research topics on Educational psychology, Masters degree

I am PhD student, I am searching my Research topic, It should be innovative,my area of interest is online education,use of technology in education

request suggestion on topic in masters in medical education .

Look at British Library as they keep a copy of all PhDs in the UK Core.ac.uk to access Open University and 6 other university e-archives, pdf downloads mostly available, all free.

May I also ask for a topic based on mathematics education for college teaching, please?

Please I am a masters student of the department of Teacher Education, Faculty of Education Please I am in need of proposed project topics to help with my final year thesis

Am a PhD student in Educational Foundations would like a sociological topic. Thank

please i need a proposed thesis project regardging computer science

Greetings and Regards I am a doctoral student in the field of philosophy of education. I am looking for a new topic for my thesis. Because of my work in the elementary school, I am looking for a topic that is from the field of elementary education and is related to the philosophy of education.

Masters student in the field of curriculum, any ideas of a research topic on low achiever students

In the field of curriculum any ideas of a research topic on deconalization in contextualization of digital teaching and learning through in higher education

Amazing guidelines

I am a graduate with two masters. 1) Master of arts in religious studies and 2) Master in education in foundations of education. I intend to do a Ph.D. on my second master’s, however, I need to bring both masters together through my Ph.D. research. can I do something like, ” The contribution of Philosophy of education for a quality religion education in Kenya”? kindly, assist and be free to suggest a similar topic that will bring together the two masters. thanks in advance

Hi, I am an Early childhood trainer as well as a researcher, I need more support on this topic: The impact of early childhood education on later academic success.

I’m a student in upper level secondary school and I need your support in this research topics: “Impact of incorporating project -based learning in teaching English language skills in secondary schools”.

Although research activities and topics should stem from reflection on one’s practice, I found this site valuable as it effectively addressed many issues we have been experiencing as practitioners.

Your style is unique in comparison to other folks I’ve read stuff from. Thanks for posting when you have the opportunity, Guess I will just book mark this site.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Research & Faculty
• Offices & Services
• Information for:
• Faculty & Staff
• News & Events
• Contact & Visit
• About the Department
• Message from the Chair
• Computer Science Major (BS/BA)
• Computer Science Minor
• Data Science and Engineering Minor
• Combined BS (or BA)/MS Degree Program
• Intro Courses
• Special Programs & Opportunities
• Student Groups & Organizations
• Undergraduate Programs
• Undergraduate Research
• Senior Thesis
• Peer Mentors
• Curriculum & Requirements
• MS in Computer Science
• PhD in Computer Science
• Admissions FAQ
• Financial Aid
• Graduate Programs
• Courses Collapse Courses Submenu
• Research Overview
• Research Areas
• Systems and Networking
• Security and Privacy
• Programming Languages
• Artificial Intelligence
• Human-Computer Interaction
• Vision and Graphics
• Groups & Labs
• Affiliated Centers & Institutes
• Industry Partnerships
• Adobe Research Partnership
• Center for Advancing Safety of Machine Intelligence
• Submit a Tech Report
• Tech Reports
• Tenure-Track Faculty
• Faculty of Instruction
• Affiliated Faculty
• Adjunct Faculty
• Postdoctoral Fellows
• PhD Students
• Outgoing PhDs and Postdocs
• Visiting Scholars
• News Archive
• Weekly Bulletin
• Monthly Student Newsletter
• All Public Events
• Seminars, Workshops, & Talks
• Distinguished Lecture Series
• CS Colloquium Series
• CS + X Events
• Tech Talk Series
• Honors & Awards
• External Faculty Awards
• University Awards
• Department Awards
• Student Resources
• Undergraduate Student Resources
• MS Student Resources
• PhD Student Resources
• Student Organization Resources
• Faculty Resources
• Postdoc Resources
• Staff Resources
• Purchasing, Procurement and Vendor Payment
• Expense Reimbursements
• Department Operations and Facilities
• Initiatives
• Student Groups
• CS Faculty Diversity Committee
• Broadening Participation in Computing (BPC) Plan
• Northwestern Engineering

CS Grad Spotlight: Dilan Nair

Nair plans to continue improving people’s quality of life and ability to connect as a software engineer with apple.

Dilan Nair enjoys developing software that makes people's lives easier.

Recently named among 12 ‘ outstanding CS seniors ,’ Nair served as a peer mentor for 10 quarters and co-directed the 2023-24 CS Mentorship Program, which aims to help computer science students cultivate a support network while they explore career pathways, gain confidence in their individual skillset, and develop strategies to achieve career objectives.

Nair was also an enthusiastic member of the WildHacks student leadership team. As director of WildHacks website development in 2023, Nair built a member management platform that included a project submission and editor dashboard. Nair took on the director role for WildHacks 2024 , overseeing all aspects of the event. Held April 5 - 7, approximately 300 participants from 14 universities dedicated the weekend to building functional and compelling software.

Nair was also the 2024 tech director of Mayfest Productions, the student organization that plans and produces Dillo Day , the nation's largest student-run music festival.

Nair graduates in June with a bachelor’s degree in computer science from the McCormick School of Engineering. We asked him about his experience at Northwestern, opportunities for impactful collaborations, and his advice for current students.

Why did you decide to pursue the CS major at McCormick?

With a bunch of fun computer science experience coming into college, I always knew that computer science was the path I wanted to take. The engineering focus intrigued me as useful knowledge that could supplement my computer science education, so I chose to go the McCormick route.

How did the McCormick curriculum help build a balanced, whole-brain ecosystem around your studies?

I really liked how the McCormick curriculum provided me with general science knowledge with an engineering focus, since it felt a lot more practical than just learning about theoretical concepts. This complemented my computer science education perfectly since all of it is practical.

What are some examples of collaborative or interdisciplinary experiences at Northwestern that were impactful to your education and research?

While I chose not to study anything outside of computer science, the opportunity to collaborate with a bunch of like-minded people was really great. I enjoy learning about other perspectives outside of my own because that helps us develop quality projects.

What skills or knowledge did you learn in the undergraduate program that you think will stay with you for a lifetime?

The undergraduate program allowed me to gain collaboration and leadership skills that I could not have learned anywhere else. The community and opportunity to easily find people with similar interests and passions was really helpful in learning how others think and how development works in a team setting.

What's next? What are your short- and long-term plans/goals in terms of graduate studies and/or your career path?

I am continuing with software engineering with a job at Apple.

What advice do you have for current Northwestern CS students?

To be successful and have a good time with computer science, it’s not about the technical skills you already have. Rather, it's the motivation and desire to build cool stuff. Being motivated gets you a long way in building quality technical skills to create cool software projects.

McCormick News Article

• Engineering Home
• CS Department
• News Article

Recent Stories

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

Publications

• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Internet & Technology

6 facts about americans and tiktok.

62% of U.S. adults under 30 say they use TikTok, compared with 39% of those ages 30 to 49, 24% of those 50 to 64, and 10% of those 65 and older.

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, whatsapp and facebook dominate the social media landscape in middle-income nations, sign up for our internet, science, and tech newsletter.

New findings, delivered monthly

Electric Vehicle Charging Infrastructure in the U.S.

64% of Americans live within 2 miles of a public electric vehicle charging station, and those who live closest to chargers view EVs more positively.

When Online Content Disappears

A quarter of all webpages that existed at one point between 2013 and 2023 are no longer accessible.

A quarter of U.S. teachers say AI tools do more harm than good in K-12 education

High school teachers are more likely than elementary and middle school teachers to hold negative views about AI tools in education.

Teens and Video Games Today

85% of U.S. teens say they play video games. They see both positive and negative sides, from making friends to harassment and sleep loss.

Americans’ Views of Technology Companies

Most Americans are wary of social media’s role in politics and its overall impact on the country, and these concerns are ticking up among Democrats. Still, Republicans stand out on several measures, with a majority believing major technology companies are biased toward liberals.

22% of Americans say they interact with artificial intelligence almost constantly or several times a day. 27% say they do this about once a day or several times a week.

About one-in-five U.S. adults have used ChatGPT to learn something new (17%) or for entertainment (17%).

Across eight countries surveyed in Latin America, Africa and South Asia, a median of 73% of adults say they use WhatsApp and 62% say they use Facebook.

5 facts about Americans and sports

About half of Americans (48%) say they took part in organized, competitive sports in high school or college.

REFINE YOUR SELECTION

Research teams, signature reports.

The State of Online Harassment

Roughly four-in-ten Americans have experienced online harassment, with half of this group citing politics as the reason they think they were targeted. Growing shares face more severe online abuse such as sexual harassment or stalking

Parenting Children in the Age of Screens

Two-thirds of parents in the U.S. say parenting is harder today than it was 20 years ago, with many citing technologies – like social media or smartphones – as a reason.

Dating and Relationships in the Digital Age

From distractions to jealousy, how Americans navigate cellphones and social media in their romantic relationships.

Americans and Privacy: Concerned, Confused and Feeling Lack of Control Over Their Personal Information

Majorities of U.S. adults believe their personal data is less secure now, that data collection poses more risks than benefits, and that it is not possible to go through daily life without being tracked.

Americans and ‘Cancel Culture’: Where Some See Calls for Accountability, Others See Censorship, Punishment

Social media fact sheet, digital knowledge quiz, video: how do americans define online harassment.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Research in Computer Science Education

• First Online: 01 January 2011

Cite this chapter

• Orit Hazzan 4 ,
• Tami Lapidot 4 &
• Noa Ragonis 5  

1995 Accesses

This chapter focuses on research in computer science education. The importance of including this topic in the MTCS course stems from the fact that computer science education research can enrich the prospective computer science teachers’ perspective with respect to the discipline of computer science, the computer science teacher’s role, and students’ difficulties, misconceptions, and cognitive abilities. Consequently, this knowledge may enhance the future work of the prospective computer science teachers in several ways, such as lesson preparation, kind of activities developed for learners, awareness to learners’ difficulties, ways to improve concept understanding, and testing and grading learners’ projects and tests. We first explain the importance of exposing the students to the knowledge gained by the computer science education research community. Then, we demonstrate different issues addressed in such research works and suggest activities to facilitate with respect to this topic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

 See http://portal.acm.org/dl.cfm

 The 2010 conference website is http://www.sigcse.org/sigcse2010/

 The 2010 conference website is: http://iticse2010.bilkent.edu.tr/

 The 2010 conference website is: http://www.issep2010.org/

 See http://portal.acm.org/toc.cfm?id=J688

See http://portal.acm.org/browse_dl.cfm?linked=1&part=magazine&idx=J1268&coll=portal&dl=ACM&CFID=77578246&CFTOKEN=38064848

 See http://www.tandf.co.uk/journals/titles/08993408.asp

 See http://portal.acm.org/browse_dl.cfm?linked=1&part=transaction&idx=J1193&coll=portal&dl=ACM

Armoni M (2009). Reduction in computer science: A (mostly) quantitative analysis of reductive solutions to algorithmic problems. JERIC 8(4): 1–30

Article   Google Scholar  

Baloian N, Luther W, Sánchez J (2002) Modeling educational software for people with disabilities: theory and practice. Proc. 5th Int. ACM Conf. Assistive Technol.: 111–118

Google Scholar  

Ben-Bassat Levy R, Ben-Ari M, Uronen PA (2003) The jeliot 2000 program animation system. Comput. & Educ. 40(1): 1–15

Blum L, Cortina T J (2007) CS4HS: An outreach program for high school CS teachers. ACM SIGCSE Bull. 39(1): 19–23

Brandes O, Vilner T, Zur E (2010) Software design course for leading CS in-service teachers. Proc. of ISSEP, Lect. Notes in Comput. Sci., Vol. 5941, 49–60

Chaffin A, Doran K, Hicks D et al (2009) Experimental evaluation of teaching recursion in a video game. Proc. ACM SIGGRAPH Symp. on Video: 79–86

Dark M J, Winstead J (2005) Using educational theory and moral psychology to inform the teaching of ethics in computing. Proc. InfoSecCD: 27–31

Denier S, Sahraoui H (2009) Understanding the use of inheritance with visual patterns. Proc. 3rd Int. Symp. Emp. Softw. Eng. and Meas.: 79–88

de Raadt M (2007) A review of Australasian investigations into problem solving and the novice programmer. Comput. Sci. Educ. 17(3): 201–213

de Raadt M, Toleman M, Watson R (2004) Training strategic problem solvers. ACM SIGCSE Bull. 36( 2): 48–51

Edwards S H (2003) Rethinking computer science education from a test-first perspective. 18th Ann. ACM SIGPLAN OOPSLA Conf.: 148–155

Erlwanger S H (1973) Benny’s conception of rules and answers in IPI mathematics. JCMB 1(2): 7–26

Fleck A (2007) Prolog as the first programming language. ACM SIGCSE Bull. 39(4): 61–64

Forišek M, Steinová M (2010) Didactic games for teaching information theory. In: Vahrenhold J (ed) Lec. Notes Comput. Sci. 5941. Springer-Verlag, Berlin, Heidelberg: 86–99

Gal-Ezer J, Harel D (1998) What (else) should CS educators know?. Communic. ACM |41(9): 77–84

Garner S, Haden P, Robins A (2005) My program is correct but it doesn’t run: A preliminary investigation of novice programmers’ problems. Proc. 7th Australasian Conf. on Comput. Educ. 42. Young A, Tolhurst D (eds): 173–180

Haberman B, Ragonis N (2010) So different though so similar? – Or vice versa? Exploration of the logic programming and the object-oriented programming paradigms. Iss. Informing Sci. and Inf. Technol. 7(2010): 393–402

Hanks B (2008) Problems encountered by novice pair programmers. JERIC 7(4): 1–13

Hauer A, Daniels M (2008) A learning theory perspective on running open ended group projects (OEGPs). Proc. 10th Conf. on Australasian Comput. Educ. – Vol. 78. Australian Compu. Soc., Darlinghurst, Australia: 85–91

Kaczmarczyk L C, Petrick E R, East J P et al (2010) Identifying student misconceptions of programming. Proc. 41st ACM Technic. Symp. on Compu. Sci. Educ.: 107–111

Kölling M, Quig B, Patterson A et al (2003) The BlueJ system and its pedagogy. Comput. Sci. Educ. 13(4): 249–268

McCauleya R, Fitzgeraldb S, Lewandowskic G et al (2008) Debugging: A review of the literature from an educational perspective. Comput. Sci. Educ.18(2): 67–92

Miller B (2007) Exploring Python as a learning and teaching language. J. Comput. in Small Coll. 22(3): 262–263

Mittermeir R T, Bischof E, Hodnigg, K (2010) Showing core-concepts of Informatics to kids and their teachers. In: Vahrenhold J (ed) Lecture notes in Comput. Sci., vol. 5941. Springer-Verlag, Berlin, Heidelberg: 143–154

Moritz S H, Blank G D (2005) A design-first curriculum for teaching Java in a CS1 course. ACM SIGCSE Bull. 37(2): 89–93

Murphy L, Lewandowski G, McCauley R et al (2008) Debugging: the good, the bad, and the quirky: A qualitative analysis of novices’ strategies. Proc. 39th SIGCSE Technic. Symp. Comput. Sci. Educ.: 163–167

Ni L (2009) What makes CS teachers change?: Factors influencing CS teachers’ adoption of ­curriculum innovations. Proc. 40th ACM Technic. Symp.Comput. Sci. Educ.: 544–548

Perkins D N, Martin F (1986) Fragile knowledge and neglected strategies in novice programmers. In: Soloway E, Iyengar S (eds.) Empirical studies of programmers. Norwood: Ablex Pub.: 213–229

Ragonis N (2010) A pedagogical approach to discussing fundamental object-oriented programming principles using the ADT SET. ACM Inroads, 1(2), 42–52

Resnick M, Maloney J, Monroy-Hernández A et al (2009) Scratch: programming for all. Commun. of the ACM 52(11): 60–67

Rodger S H, Bashford M, Dyck L et al (2010) Enhancing K-12 education with Alice programming adventures. Proc. ITiCSE: 234–238

Samurçay R (1989) The concept of variable in programming: its meaning and use in problem-solving by novice programmers. In: Soloway E, Spohrer JC. (eds) Studying the novice ­programmer, Lawrence Erlbaum Associates, New Jersey, 161–178

Shulman L S (1986) Those who understand: knowledge growth in teaching. J. Educ. Teach. 15(2): 4–14

Shulman L S (1990) Reconnecting foundations to the substance of teacher education. Teach. Coll. Record 91(3): 300–310

Smith J P III, diSessa A A, Roschelle J (1993) Misconceptions reconceived: A constructivist analysis of knowledge in transition. J. Learning Sci. 3(2): 115–163

Soh L, Samal A, Nugent G (2005) A framework for CS1 closed laboratories. JERIC 5(4): 2

Stolin Y, Hazzan O (2007) Students’ understanding of computer science soft ideas: the case of programming paradigm. ACM SIGCSE Bull. 39(2): 65–69

Van Roy P, Armstrong J, Flatt M et al (2003) The role of language paradigms in teaching programming. Proc. 34th SIGCSE Tech. Symp. Comput. Sci. Educ.: 269–270

Voyles M M, Haller S M, Fossum T V (2007) Teacher responses to student gender differences. Proc. 12th Ann. SIGCSE Conf. on Innovation & Technol. in Comput. Sci. Educ.: 226–230

Download references

Author information

Authors and affiliations.

Dept. Education in Technology & Science, Technion - Israel Institute of Technology, Technion City, Haifa, Israel

Dr. Orit Hazzan ( Associate Professor ) & Dr. Tami Lapidot

Computer Science Studies, School of Education, Beit Berl College, Doar Beit Berl, Israel

Dr. Noa Ragonis

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Orit Hazzan .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Hazzan, O., Lapidot, T., Ragonis, N. (2011). Research in Computer Science Education. In: Guide to Teaching Computer Science. Springer, London. https://doi.org/10.1007/978-0-85729-443-2_4

Download citation

DOI : https://doi.org/10.1007/978-0-85729-443-2_4

Published : 22 March 2011

Publisher Name : Springer, London

Print ISBN : 978-0-85729-442-5

Online ISBN : 978-0-85729-443-2

eBook Packages : Computer Science Computer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

New open-source platform allows users to evaluate performance of AI-powered chatbots

Researchers have developed a platform for the interactive evaluation of AI-powered chatbots such as ChatGPT.

A team of computer scientists, engineers, mathematicians and cognitive scientists, led by the University of Cambridge, developed an open-source evaluation platform called CheckMate, which allows human users to interact with and evaluate the performance of large language models (LLMs).

The researchers tested CheckMate in an experiment where human participants used three LLMs -- InstructGPT, ChatGPT and GPT-4 -- as assistants for solving undergraduate-level mathematics problems.

The team studied how well LLMs can assist participants in solving problems. Despite a generally positive correlation between a chatbot's correctness and perceived helpfulness, the researchers also found instances where the LLMs were incorrect, but still useful for the participants. However, certain incorrect LLM outputs were thought to be correct by participants. This was most notable in LLMs optimised for chat.

The researchers suggest models that communicate uncertainty, respond well to user corrections, and can provide a concise rationale for their recommendations, make better assistants. Human users of LLMs should verify their outputs carefully, given their current shortcomings.

The results, reported in the Proceedings of the National Academy of Sciences (PNAS) , could be useful in both informing AI literacy training, and help developers improve LLMs for a wider range of uses.

While LLMs are becoming increasingly powerful, they can also make mistakes and provide incorrect information, which could have negative consequences as these systems become more integrated into our everyday lives.

"LLMs have become wildly popular, and evaluating their performance in a quantitative way is important, but we also need to evaluate how well these systems work with and can support people," said co-first author Albert Jiang, from Cambridge's Department of Computer Science and Technology. "We don't yet have comprehensive ways of evaluating an LLM's performance when interacting with humans."

The standard way to evaluate LLMs relies on static pairs of inputs and outputs, which disregards the interactive nature of chatbots, and how that changes their usefulness in different scenarios. The researchers developed CheckMate to help answer these questions, designed for but not limited to applications in mathematics.

"When talking to mathematicians about LLMs, many of them fall into one of two main camps: either they think that LLMs can produce complex mathematical proofs on their own, or that LLMs are incapable of simple arithmetic," said co-first author Katie Collins from the Department of Engineering. "Of course, the truth is probably somewhere in between, but we wanted to find a way of evaluating which tasks LLMs are suitable for and which they aren't."

The researchers recruited 25 mathematicians, from undergraduate students to senior professors, to interact with three different LLMs (InstructGPT, ChatGPT, and GPT-4) and evaluate their performance using CheckMate. Participants worked through undergraduate-level mathematical theorems with the assistance of an LLM and were asked to rate each individual LLM response for correctness and helpfulness. Participants did not know which LLM they were interacting with.

The researchers recorded the sorts of questions asked by participants, how participants reacted when they were presented with a fully or partially incorrect answer, whether and how they attempted to correct the LLM, or if they asked for clarification. Participants had varying levels of experience with writing effective prompts for LLMs, and this often affected the quality of responses that the LLMs provided.

An example of an effective prompt is "what is the definition of X" (X being a concept in the problem) as chatbots can be very good at retrieving concepts they know of and explaining it to the user.

"One of the things we found is the surprising fallibility of these models," said Collins. "Sometimes, these LLMs will be really good at higher-level mathematics, and then they'll fail at something far simpler. It shows that it's vital to think carefully about how to use LLMs effectively and appropriately."

However, like the LLMs, the human participants also made mistakes. The researchers asked participants to rate how confident they were in their own ability to solve the problem they were using the LLM for. In cases where the participant was less confident in their own abilities, they were more likely to rate incorrect generations by LLM as correct.

"This kind of gets to a big challenge of evaluating LLMs, because they're getting so good at generating nice, seemingly correct natural language, that it's easy to be fooled by their responses," said Jiang. "It also shows that while human evaluation is useful and important, it's nuanced, and sometimes it's wrong. Anyone using an LLM, for any application, should always pay attention to the output and verify it themselves."

Based on the results from CheckMate, the researchers say that newer generations of LLMs are increasingly able to collaborate helpfully and correctly with human users on undergraduate-level maths problems, as long as the user can assess the correctness of LLM-generated responses. Even if the answers may be memorised and can be found somewhere on the internet, LLMs have the advantage of being flexible in their inputs and outputs over traditional search engines (though should not replace search engines in their current form).

While CheckMate was tested on mathematical problems, the researchers say their platform could be adapted to a wide range of fields. In the future, this type of feedback could be incorporated into the LLMs themselves, although none of the CheckMate feedback from the current study has been fed back into the models.

"These kinds of tools can help the research community to have a better understanding of the strengths and weaknesses of these models," said Collins. "We wouldn't use them as tools to solve complex mathematical problems on their own, but they can be useful assistants, if the users know how to take advantage of them."

The research was supported in part by the Marshall Commission, the Cambridge Trust, Peterhouse, Cambridge, The Alan Turing Institute, the European Research Council, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

• Computer Modeling
• Mathematics
• Computers and Internet
• Mathematical Modeling
• Neural Interfaces
• Communications
• Distributed Computing
• Computer Science
• Computer security
• Scientific visualization
• Mathematical model
• John von Neumann
• Videoconferencing
• Virtual reality
• Game theory

Story Source:

Materials provided by University of Cambridge . The original text of this story is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . Note: Content may be edited for style and length.

Journal Reference :

• Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik. Evaluating language models for mathematics through interactions . Proceedings of the National Academy of Sciences , 2024; 121 (24) DOI: 10.1073/pnas.2318124121

Cite This Page :

Explore More

• Myelination May Drive Drug Addiction
• Freshwater On Earth 4 Billion Years Ago
• Extended Battle: 3,500-Year-Old Mycenaean Armor
• Oral Insulin Drops: Relief for Diabetes Patients
• AIs Are Irrational, but Not Like Humans
• Bronze Age Cuisine of Mongolian Nomads
• Poor Quality Diet Makes Our Brains Sad
• AI Improves Performance Across Fusion Devices
• New Explanation for Why Earthquakes Happen
• Electrified Charcoal 'Sponge' Soaks Up CO2 ...

Trending Topics

Strange & offbeat.