hypothesis of the study about online learning

• Research article
• Open access
• Published: 02 October 2020

Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction

• Hassan Abuhassna   ORCID: orcid.org/0000-0002-5774-3652 1 ,
• Waleed Mugahed Al-Rahmi 1 ,
• Noraffandy Yahya 1 ,
• Megat Aman Zahiri Megat Zakaria 1 ,
• Azlina Bt. Mohd Kosnin 1 &
• Mohamad Darwish 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  38 ( 2020 ) Cite this article

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This research aims to explore and investigate potential factors influencing students’ academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom’s Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in higher education. This research utilized a quantitative research method. The model of this research illustrates eleven factors on using online learning platforms to improve students’ academic achievements and satisfaction. The findings showed that the students’ background, experience, collaborations, interactions, and autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, remembering, understanding, analyzing, and satisfaction was positively aligned with students’ academic achievements. Consequently, the empirical findings present a strong support to the integrative association between TDT and BTT theories in relation to using online learning platforms to improve students’ academic achievements and satisfaction, which could help decision makers in universities and higher education and colleges to plan, evaluate, and implement online learning platforms in their institutions.

Introduction

Higher education organizations over the previous two decades have offered full courses online as an integral part of their curricula, besides encouraging the completion throughout the online courses. Additionally, the number of students who are not participating in any courses online has continued to drop over the past few years. Similarly, it is perfectly possible to state that learning online is obviously an educational platform (Allen, Seaman, Poulin, & Straut, 2016 ). Courses online are trying to connect social networking components, experts’ content, because online resources are growing on daily basis. Such courses depend on active participation of a significant number of learners who participate independently in accordance with their education objectives, skills, and previous background and experience (McAuley, Stewart, Siemens, & Cormier, 2010 ). Nevertheless, learners differ in their previous background and experience, along with their education techniques, which clearly influence their online courses results besides their achievement (Kauffman, 2015 ). Consequently, despite the online learning evolution, learning online possibly will not be appropriate for each learner (Bouhnik & Carmi, 2013 ). Nevertheless, while online learning application among academic world has grown rapidly, not enough is identified regarding learners’ previous background and experience in learning online. Not so long ago, investigation concentrated on particular characteristics of learners’ experiences along with beliefs, for instance collaboration with their own instructor, online course quality, or studying with a certain learning management system (LMS) (Alexander & Golja, 2007 ; (Lester & King, 2009 ). Generally, limited courses or a single institution were investigated (Coates, James, & Baldwin, 2005 ; Lee, Yoon, & Lee, 2009 ). Few studies examined bigger sample sizes between one or more particular institutes (Alexander & Golja, 2007 ). Additionally, there is a shortage of researches that examine learners’ previous background and experience comparing face-to-face along with learning online elements, e.g., (Bliuc, Goodyear, & Ellis, 2007 ). The development of learners’ previous background and experience, skills, are realized to be the major advantages for administrative level for learning online.

Similarly, learners’ satisfaction and academic achievement towards learning online attracted considerable attention from scholars who employed several theoretical models in order to evaluate learners’ satisfaction and academic achievements (Abuhassna, Megat, Yahaya, Azlina, & Al-rahmi, 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi, Othman, & Yusuf, 2015a ; Al-Rahmi, Othman, & Yusuf, 2015b ). This present study highlights the effects of online learning platforms on student’s satisfaction, in relation to their background and prior experiences towards online learning platforms to identify learners that are going to be satisfied toward online course. Furthermore, this research explores the effects of transactional distance theory (TDT); student collaboration, student- instructor dialogue or communication, and student autonomy in relation to their satisfaction. Accordingly, this study investigates students’ academic achievements within online platforms, utilizing Bloom theory to measure students’ achievements through four main components, namely, understanding, remembering, applying, and analyzing. This study could have a significant influence on online course design and development. Additionally, this research may influence not only academic online courses but then other educational organizations according to the fact that several organizations offer training courses and solutions online. Both researchers and Instructors will be able to utilize and elaborate in accordance with the preliminary model, which was developed throughout this research, on the effects of online platforms on student’s satisfaction and academic achievements. Advantages of online learning and along with its applications were mentioned in earlier correlated literature (Abuhassna et al., 2020 ;Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). However, despite the growing usage of online platforms, there is a shortage of employing this technology, which creates an issue in itself (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). Consequently, the research problem lies in the point that a model needs to be created to locate the significant evidence based on the data of student’s background, experiences and interactions within online learning environments which influence their academic performance and satisfaction. Thus, this developed model must be as a guidance for instructors and decision makers in the online education industry in terms of using online platforms to improve students learning experience through online platforms. Bearing in mind these conditions, our major problem was: how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

Research questions

The major research question that are anticipated to be answered is:

how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

To be able to answer this question, it is required to examine numerous sub-questions which have been stated as follow:

Q1: What is the relationship between students’ background and students’ satisfaction?

Q2: What is the relationship between students’ experience and students’ satisfaction?

Q3: What is the relationship between students’ collaboration and students’ satisfaction?

Q4: What is the relationship between students’ interaction and students’ satisfaction?

Q5: What is the relationship between students’ autonomy and students’ satisfaction?

Q6: What is the relationship between students’ satisfaction and students’ academic achievements?

Q7: What is the relationship between students’ application and students’ academic achievements?

Q8: What is the relationship between students’ remembering and students’ academic achievements?

Q9: What is the relationship between students’ understanding and students’ academic achievements?

Q10: What is the relationship between students’ analyzing and students’ academic achievements?

Research theory and hypotheses development

When designing web-courses within online learning instructions or mechanisms in general, educators are left with several decisions and considerations to face, which accordingly affect how students experience instruction, how they construct and process knowledge, how students could be satisfied through this experiment, and how web-based learning courses could enhance their academic achievements. In this study, we construct our theoretical framework according to Moore transactional distance theory (TDT) to measure student’s satisfaction, in addition to Bloom theory components to measure students’ academic achievements. Though the origins of TDT can be traced to the work of Dewey, it is Michael Moore who is identified as the innovator of this theory that first appeared in 1972. In his study and development of the theory, he acknowledged three main components of TDT that work as the base for much of the research on DL. Also, Bloom’s Taxonomy was established in 1956 under the direction of educational psychologist to measure students’ academic achievement (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956 ). TDT theory has been selected in this study since Transactional distance’s term indicates the geographical space between the student and instructor. Based on the learning understanding, which happens through learner’s interaction with his environment. This theory considers the role of each of these elements (Student’s autonomy, Dialogue, and class structure) whereas these three elements could help to investigate student’s satisfaction. Moore’s ( 1990 ) notion of ‘Transactional Distance’ adopt the distance that happens in all relations in education. The distance in the theory is mainly specified the dialogue’s amount which happens between the student and the teacher, and the structure’s amount in the course design. Which serves the main goal of this study as to enhance students online learning experience in relation to their satisfaction. Whereas, Bloom Theory has been selected in this study in addition to TDT to enhance students online learning experience in relation to their student’s achievements. In a conclusion both methods were implemented to develop and hypothesis this study hypothesis. See Fig.  1 .

Research Model and Hypotheses

Hypothesis of the study

H1: There is a significant relationship between students’ background and students’ satisfaction.

H2: There is a significant relationship between students’ experience and students’ satisfaction.

H3: There is a significant relationship between students’ collaboration and students’ satisfaction.

H4: There is a significant relationship between students’ interaction and students’ satisfaction.

H5: There is a significant relationship between students’ autonomy and students’ satisfaction.

H6: There is a significant relationship between students’ satisfaction and students’ academic achievements.

H7: There is a significant relationship between students’ application and students’ academic achievements.

H8: There is a significant relationship between students’ remembering and students’ academic achievements.

H9: There is a significant relationship between students’ understanding and students’ academic achievements.

H10: There is a significant relationship between students’ analyzing and students’ academic achievements.

Hypothesis developments and literature review

This Section of the study will discuss the study hypothesis and relates each hypothesis to its related studies from the literature.

Students background toward online platforms

Students’ background regarding online platforms in this study is referred to as their readiness and willingness to use and adapt to different online platforms, providing them with the needed support and assistance. Students’ background towards online learning is a crucial component throughout this process, as prior research revealed that there are implementation issues, for instance; the deficiency of qualified lecturers, infrastructure and facilities, in addition to students’ readiness, besides students’ resistance to accept online learning platforms in addition to the Learning Management System (LMS) platforms, as educational tools (Azhari & Ming, 2015 ). However, student demand continued to increase, spreading to global audiences due to its exceptional functionality, flexibility and eventual accessibility (Azhari & Ming, 2015 ). There have been persistent apprehensions regarding online learning quality compared with traditional learning settings. In their research, (Paechter & Maier, 2010 ; Panyajamorn, Suthathip, Kohda, Chongphaisal, & Supnithi, 2018 ) have discovered that Austrian learners continue to prefer traditional learning environments due to communication goals, along with the interpersonal relations preservation. Moreover, (Lau & Shaikh, 2012 ) have discovered that Malaysian learners’ internet efficiency and computer skills, along with their personal demographics like gender, background, level of the study, as well as their financial income lead to a significant difference in their readiness towards online learning platforms. Abuhassna and Yahaya ( 2018 ) claimed that the current technologies in education play an essential role in providing a full online learning experience which is close enough to a face-to-face class in spite of the physical separation of the students from their educator, along with other students. Platforms of online learning lend themselves towards a less hierarchical methodology in education, fulfilling the learning desires of individuals which do not approach new information in a linear or a systematic manner. Platforms of online learning additionally are the most suitable ways for autonomous students (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Paechter & Maier, 2010 ; Panyajamorn et al., 2018 ).

Students experience toward online platforms

Students’ experience in the current research indicates that learners must have prior experience in relation to utilizing online learning platform in their education settings. Thus, students experience towards online learning offers several advantages among themselves and their instructors in strengthening students’ learning experiences especially for isolated learners (Jaques & Salmon, 2007 ; Lau & Shaikh, 2012 ; Salmon, 2011 ; Salmon, 2014 ). Regardless of student recognition of the advantages towards supporting their learning throughout utilizing the technology, difficulties may occur through the boundaries about their technical capabilities and prior experiences towards utilizing the software itself from the perspective of its functionality. As demonstrated over learner’s experience and feedback from several online sessions over the years, this may frequently become a frustration source between both learners and their instructors, as this may make typically uncomplicated duties, for instance, watching a video, uploading a document, and other simple tasks to be progressively complicated for them, having no such prior experience. Furthermore, when filling out evaluations, for instance, online group presentations, the relatively limited capability to communicate face-to-face then to rely on a non-verbal signal along with audience’s body language might be a discouraging component. Nonetheless, the significance of being in a position to participate with other colleagues employing online sessions, which are occasionally nonvisual, for instance; teleconference format is a progressively significant skill in the modern workplace, thus affirming the importance of concise, clear, intensive interactions skills (Salmon, 2011 ; Salmon, 2014 ).

Student collaboration among themselves in online platforms

Students’ collaborations in the current study refers to the communication and feedback among themselves in online platforms. To refine and measure transactional distance using a survey tool, (Rabinovich, 2009 ) created a survey instrument to measure transactional distance in a higher education setting. A survey was sent to 235 students enrolled in a synchronous web-based graduate class in business regarding transactional distance and Collaborations (Rabinovich, 2009 ). The synchronous learning environment was described as a place where “live on-campus classes are conveyed simultaneously to both in-class students on campus and remote students on the Web who join via virtual classroom Web collaboration software” (Rabinovich, 2009 ). The virtual classroom software is similar to the characteristics of the two different software described by (Falloon, 2011 ; Mathieson, 2012 ) that it allows for students to interact with the educator and fellow students in real-time (Rabinovich, 2009 ). Moreover, (Kassandrinou, Angelaki, & Mavroidis, 2014 ) reported that the instructor plays a crucial role as interaction and communication helpers, as they are tasked with fostering, reassuring and assisting communication and interaction among students. Face-to-face tutorials have proven to be a vast opportunity for a multitude of students to interchange ideas, argue the content of the course and its related concerns (Vasala & Andreadou, 2010 ).

Students’ interactions with the instructor in online platforms

Purposeful interaction or (dialogue) in the current study describes communication that is learner-learner and learner-instructor which is designed to improve the understanding of the student. According to (Shearer, 2010 ) communication should also be constructive in that it builds upon ideas and work from others, as well as assists others in learning. (Moore, 1972 ) affirmed that learners also must realize that, and value the importance of the learning interactions as a vital part of the learning process. In a manner similar to (Benson & Samarawickrema, 2009 ] study of teacher preparatory students, (Falloon, 2011 ) investigated the use of digital tools in a case study at a teacher education program in New Zealand. (Mathieson, 2012 ) also explored the role dialogue plays in digital learning environments. She created a digital survey that examined students’ perception of audio-visual feedback in courses that utilize screen casting digital tools. (Moore, 2007 ) discusses autonomous learners searching for courses that do not stress structure and dialogue in order explain and enhance their learning progression. (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2015b ; Al-Rahmi, Othman, & Yusuf, 2015d ; Furnborough, 2012 ) concluded that the feeling of cooperation that learners’ share with their fellow students effect their reaction concerning their collaboration with their peers.

Student autonomy in online platforms

Student autonomy in the current study refers to their independence and motivation towards learning. The learner is the motivation of the way toward learning, along with their expectations and requirements, thinking about everyone as a unique individual and hence investigating their own capacities and possibilities. Thus, extraordinary importance is attributed to autonomy in DL environments, since the option of instructive intercession offered in distance education empowers students towards learning autonomy (Massimo, 2014 ). In this respect, the connection between autonomy of student and explicit parts of the learning procedure are in the center of consideration as mentioned. (Madjar, Nave, & Hen, 2013 ) concluded that a learners’ autonomy-supportive environment provides these learners with adoption of a more aims guided learning, leading to more learning achievements. This is why autonomy is desired in the online settings for both individual development and greater achievement in academic environments. The researchers also indicate in their research that while autonomy supports outcomes in goals and aims guiding, educator practices mainly lead to goals which necessary cannot adapt. Thus, supportive-autonomy learning process needs to be designed with affective elements consideration as well. However, (Stroet, Opdenakker, & Minnaert, 2013 ) efficiently surveyed 71 experimental studies on the impacts of autonomy supportive teaching on motivation of learner and discovered a clear positive correlation. Similar to attribution theory, the relationship between learner control and inspiration involves the possibility of learners adjusting their own inspirations, for example, learners may be competent to change self-determined extrinsic motivation to intrinsic motivation. However, (Jacobs, Renandya, & Power, 2016 ) further indicated that learners will not reach the same level of autonomy without reviewing learner’s autonomy insights, reflecting on their learning experiences, sharing these experiences and reflections with other learners, and realizing the elements influencing all these processes, and the process of learning as well.

Student satisfaction in online platforms

Student satisfaction in the current study refers to the fact that there are many factors that play a role in determining the learner’s satisfaction, such as faculty, institution, individual learner element, interaction/communication elements, the course elements, and learning environment. Discussion of the elements also related to the role of the instructor, with the learner’s attitude, social presence, usefulness, and effectiveness of Online Platforms. (Yu, 2015 ) investigated that student satisfaction was positively associated with interaction, self-efficacy and self-regulation without significant gender variations. (Choy & Quek, 2016 ). examined the relationships between the learners’ perceived teaching, social, and cognitive element. In addition, satisfaction, academic performance, and achievement can be measured using a revised form of the survey instrument. (Kirmizi, 2014 ) studied connection between 6 psychosocial scales: personal relevance, educator assistance, student interaction and collaboration, student autonomy, authentic learning, along with active learning. A moderate level of correlation was found between these mentioned variables. Learner satisfaction predictors were educator support, personal relevance and authentic learning, while authentic learning was the only academic success predictor. Findings of (Bordelon, 2013 ) determined and described a positive correlation between both achievement and satisfaction. He demonstrated that the reasons behind these conclusions could be cultural variations in learner’s satisfaction which point out learning accession Zhu ( 2012 ). Scholars in the field of student satisfaction emphasis on the delivery besides the operational side of the student’s experience in the teaching process (Al-Rahmi, Othman, & Yusuf, 2015e ).

Students’ academic achievements in online platforms

Students achievements in this study refers to Bloom’s main four components of achievements, which are remembering, understanding, applying, and analyzing. Finding in a study conducted by (Whitmer, 2013 ) revealed the relationships between student academic achievement and the LMS usage, thus the findings showed a highly systematic association ( p  < .0000) in relation to every variable. These variables described 12% and 23% of variations within the final course marks, which indicates that learners who employed the LMS more often obtained higher marks than the others. Thus, the correlation techniques examined these variables separately to ascertain their association with the final mark. Moreover, it is not the technology itself; it is the educational methods in relation to which technology has been utilized that create a change in learners’ achievement. Instruments used are significant in identifying the technology impact, moreover, it is the implementation of those instruments under specific activities and for certain purposes which indicates whether or not they are effective. In contrast, a study conducted by (Barkand, 2017 ) revealed that LMS tools were not considered to have an effect on semester final grades when categorized by school year. In his study, semester final grades were a measure of student achievement, which has subjective elements. To account for the subjective elements in semester final grades, the study also included objective post test scores to evaluate student learning. Additionally, in this study, we refer to Bloom’s Taxonomy established in 1956 under the direction of educational psychologist for measuring students’ academic achievement (Bloom et al., 1956 ). Moreover, in this study, we selected fours domains of Blooms Taxonomy in order to achieve this study objectives, which are; application: which refers to using a concept in new context, for instance; applying what has been learned inside the classroom into different circumstances; remembering, which refers to recalling or retrieving prior learned knowledge; understanding, which refers to realizing the meaning, then clarification of problems instructions; analyzing, which refers to separating concepts or material into parts in such a way that its structure can be distinguished, understood among inferences and facts.

Students’ application

Applying involves “carrying out or using a procedure through executing or implementing” (Anderson & Krathwohl, 2001 ). Applying in this study refers to the student’s ability to use online platforms, such as how to log in, how to end session, how to download materials, how to access links and videos. Students can exchange information about a specific topic in online platforms such as Moodle, Google Documents, Wikis and apply knowledge to create and participate in online platforms.

Students’ remembering

Remembering is defined as “retrieving, recognizing, and recalling relevant knowledge from long-term memory” (Anderson & Krathwohl, 2001 ). In this study, remembering is referred to the ability to organize and remember online resources to easily find information on the internet. Moreover, students can easily cooperate with their colleagues and educator, contributing to the educational process and justifying their study procedure. Anderson and Krathwohl ( 2001 ) In their review of Bloom’s taxonomy, Anderson and Krathwohl ( 2001 ) recognized greater learning levels as creating, evaluating, and analyzing, with the lower learning levels as applying, understanding, and remembering.

Students’ understanding

Understanding involves “constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining” (Anderson & Krathwohl, 2001 ). In this study, understanding is referred to as understanding regarding a subject then putting forward new suggestions about online settings, for instance; understanding how e-learning works, or LMS. For example, students use online platforms to review concepts, courses, and prominent resources are being used inside the classroom environment.

Students’ analyzing

Analyzing includes “breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing” (Anderson & Krathwohl, 2001 ). Analyzing refers to the student’s ability to connect, discuss, mark-up, then evaluate the information received into one certain workplace or playground. Solomon and Schrum ( 2010 ) claim that educators have started employing online platforms for a range of activities, since they have become more familiar and there are ways for learners to benefit from using them. Generally, the purpose and goal are to publicize the development types, innovation, as well as additional activities that their learners usually do independently. Such instruments have also provided instructors ways to encourage and promote genuine cooperation in their project’s development (Solomon & Schrum, 2010 ).

Research methodology

A quantitative approach was implemented in this study to provide an inclusive insight in relation to students online learning experience and how to enhance both their satisfaction and academic achievements using a questionnaire. Two experts were referred for the evaluation of the questionnaire’s content. Before the collection of the data, permission regarding the current research purpose has been obtained from Universiti Teknologi Malaysia (UTM). In relation to the sampling and population, this research was conducted among undergraduate learners who have been online learning users. Learners, who had manually obtained the questionnaires, have been requested to fill in their details, then fill their own assessments regarding online learning platforms and its effects towards their academic achievements. Thus, for data analysis, the data that were attained from questionnaires were then analyzed using the Statistical Package for the Social Sciences (SPSS). Specifically, Structural Equation Modeling (SEM- Amos), which has been employed as a primary data analysis tool. Moreover, utilizing SEM-Amos process involves two main phases: evaluating construct validity, the convergent validity, along with the discriminant validity of the measurements; then analyzing the structural model. These mentioned two phases followed the recommendations of (Bagozzi, Yi, & Nassen, 1998; Hair, Sarstedt, Ringle, & Mena, 2012a , 2012b ).

Sample characteristics and data collection

A total of 283 questionnaires were distributed manually; of these, only 264, which make up 93.3% of the total number, were returned to the authors. Excluding the 26 incomplete questionnaires, 264 were evaluated employing SPSS. A total of 21 questionnaires have been excluded: 14 were incomplete and 7 having outliners. Thus, the overall number of valid questionnaires was 243 following this exclusion. This exclusion step is being supported by Hair et al. ( 2012a , 2012b ) . Moreover, Venkatesh, Thong, & Xu, 2012 who pointed out that this procedure is essential to be implemented as the existence of outliers could be a reason for inaccurate results. Regarding the respondent’s demographic details: 91 (37.4%) were males, and 152 (62.6%) were females. 149 (61.3%) were in the age range of 18 t0 20 years old, 77 (31.7%) were in the age range of 21 to 24 years old, and 17 (7.0%) were in the age range of 25 to 29 years old. Regarding level of study: 63 (25.9%) were from level 1, 72 (29.6%) were from level 2, 50 (20.6%) were from level 3, and 58 (23.9%) were from level 4.

Measurement instruments

The questionnaire in this study has been developed to fit the study hypothesis. Consequently, it was developed based into both theories that have been utilized in this study. The questionnaire has two main sections, first section aims to measure student satisfaction which is based on the TDT theory variables. Second section of the questionnaire has been developed to measure students’ academic achievement based on Bloom theory. According to Bloom theory there are four variables that measure students’ achievements, which are application, remembering, understanding, analyzing. On that basis the questionnaire has been developed to measure both students’ satisfaction and academic achievements . The construct items were adapted to ensure content validity. This questionnaire consisted of two main sections. First part covered the demographic details of the respondents’ including age, gender, educational level. The second part comprises 51 items which were adapted from previous researches as following; student background, five items, student experience, five items adapted from (Akaslan & Law, 2011 ), student collaborations, and, student interactions items adapted from (Bolliger & Inan, 2012 ), student autonomy, five items adapted from (Barnard et al., 2009 ; Pintrich, Smith, Garcia, & McKeachie, 1991 ), student satisfaction, six items adapted from (The blended learning impact evaluation at UCF is conducted by Research Initiative for Teaching Effectiveness, n.d. ). Moreover, effects of the students’ application, four items, students’ remembering, four items, students’ understanding, four items, students’ analyzing, four items, and students’ academic achievements, four items adapted from (Pekrun, Goetz, & Perry, 2005 ). The questionnaire has been distributed to the students after taking the online course.

Result and analysis

Cronbach’s Alpha reliability coefficient result was 0.917 among all research model factors. Thus, the discriminant validity (DV) assessment was carried out through utilizing three criteria, which are: index between variables, which is expected to be less than 0.80 (Bagozzi, Yi, & Nassen, 1988 ); each construct AVE value must be equal to or higher than 0.50; square of (AVE) between every construct should be higher, in value, than the inter construct correlations (IC) associated with the factor [49]. Furthermore, the crematory factor analysis (CFA) findings along with factor loading (FL) should therefore be 0.70 or above although the Cronbach’s Alpha (CA) results are confirmed to be ≥0.70 [50]. Researchers have also added that composite reliability (CR) is supposed to be ≥0.70.

Model analysis

Current research employed AMOS 23 to analyze the data. Both structural equation modeling (SEM) as well as confirmatory factor analysis (CFA) have been employed as the main analysis tools. Uni-dimensionality, reliability, convergent validity along with discriminant validity have been employed to assess the measurement model. (Bagozzi et al., 1988 ; Byrne, 2010 ; Kline, 2011 ) highlighted that goodness-of-fit guidelines, such as the normed chi-square, chi-square/degree of freedom, normed fit index (NFI), relative fit index (RFI), Tucker-Lewis coefficient (TLI) comparative fit index (CFI), incremental fit index (IFI), the parsimonious goodness of fit index (PGFI), thus, the root mean square error of approximation (RMSEA) besides the root mean-square residual (RMR). All these are tools which could be utilized as the assessment procedures for the model estimation. See Table  1 & Fig.  2 .

Measurement Model

Measurement model

Such type of validity is commonly employed to specify the size difference between a concept and its indicators and other concepts (Hair et al., 2012a , 2012b ). Through analysis in this context, discriminant validity has proven to be positive over all concepts given that values have been over 0.50 (cut-off value) from p  = 0.001 according to Fornell and Larcker ( 1981 ). In line with Hair et al. ( 2012a , 2012b ) . Bagozzi, Yi, & Nassen, (1998), the correlation between items at any two specified constructs must not exceed the square root of the average variance that is shared between them in a single construct. The outcomes values of composite reliability (CR) besides those of Cronbach’s Alpha (CA) remained about 0.70 and over, while the outcomes of the average variance extracted (AVE) remained about 0.50 and higher, indicating that all factor loadings (FL) were significant, thereby fulfilling conventions in the current assessment Bagozzi, Yi, & Nassen, (1998), and Byrne ( 2010 ). Following sections expand on the results of the measurement model. Findings of validity, reliability, average variance extracted (AVE), composite reliability (CR) as well as Cronbach’s Alpha (CA) have all been accepted, which also demonstrated determining the discriminant validity. It is determined that all the values of (CR) vary between 0.812 and 0.917, meaning they are above the cut-off value of 0.70. The (CA) result values also varied between 0.839 and 0.897 exceeding the cut-off value of 0.70. Thus, the (AVE) was similarly higher than 0.50, varying between 0.610 and 0.684. All these findings are positive, thus indicating significant (FLs) and they comply with the conventional assessment guidelines Bagozzi, Yi, & Nassen, (1998), along with Fornell and Larcker ( 1981 ). See Table  2 and Additional file  1 .

Structural model analysis

In the current study, the path modeling analysis has been utilized to examine the impact of students’ academic achievements among higher education institutions through the following factors (students’ background, students’ experience, students’ collaborations, students’ interaction, students’ autonomy, students’ remembering, students’ understanding, students’ analyzing, students’ application, students’ satisfaction), which is based on online learning. The findings are displayed then compared in hypothesis testing discussion. Subsequently, as the second stage, factor analysis (CFA) has being conducted on structural equation modeling (SEM) in order to assess the proposed hypotheses as demonstrated in Fig.  3 .

Findings for the Proposed Model Path analysis

As shown in both Figs.  3 and 4 , all hypotheses have been accepted. Moreover, Table  3 below shows that the fundamental statistics of the model was good, which indicates model validity along with the testing results of the hypotheses through demonstrating the values of unstandardized coefficients besides standard errors of the structural model.

Findings for the Proposed Model T.Values

The first direct five assumptions, students’ background, students’ experience, students’ collaborations, students’ interaction; students’ autonomy with students’ satisfaction, were addressed. In accordance with Fig.  4 and Table 3 , relations between students’ background and students’ satisfaction was (β = .281, t = 5.591, p  < 0.001), demonstrating that the first hypothesis (H1) has suggested a positive and significant relationship. Following hypothesis illustrated the relationship between students’ experience and students’ satisfaction (β = .111, t = 1.951, p  < 0.001), demonstrating that the second hypothesis (H2) proposed a positive and significant relationship. Third hypothesis illustrated the relationship between students’ collaborations and students’ satisfaction (β = .123, t = 2.584, p  < 0.001) demonstrating that the third hypothesis (H3) has suggested a positive and significant relationship. Additionally, the relationship between students’ background and students’ satisfaction was (β = .116, t = 2.212, p < 0.001), indicating that the fourth hypothesis (H4) has suggested a positive and significant relationship. Further to the above-mentioned findings, the relationship between students’ autonomy and students’ satisfaction was (β = .470, t = 7.711, p  < 0.001), demonstrating that the fifth hypothesis (H5) has suggested a positive and significant relationship. Moreover, in the second section, five assumptions were discussed, which are students’ satisfaction, students’ remembering, students’ understanding, students’ analyzing, students’ application along with students’ academic achievements.

As shown in Fig. 4 and Table 3 , the association between students’ satisfaction and students’ academic achievements was (β = .135, t = 3.473, p  < 0.001), demonstrating that the sixth hypothesis (H6) has suggested a positive and significant relationship. Following hypothesis indicated the relationship between students’ application and students’ academic achievements (β = .215, t = 6.361, p  < 0.001), indicating that the seventh hypothesis (H7) has suggested a positive and significant relationship. Thus, the eighth hypothesis indicated the relationship between students’ remembering and students’ academic achievements was (β = .154, t = 4.228, p  < 0.001), demonstrating that the eight hypothesis (H8) has suggested a positive and significant relationship. Additionally, the correlation between students’ understanding and students’ academic achievements was (β = .252, t = 6.513, p < 0.001), demonstrating that the ninth hypothesis (H9) has suggested a positive and significant relationship. Finally, the relationship between students’ analyzing and students’ academic achievements was (β = .179, t = 6.215, p < 0.001), demonstrating that the tenth hypothesis (H10) has suggested a positive and significant relationship. Accordingly, this current model demonstrated student’s compatibility to use online learning platforms to improve students’ academic achievements and satisfaction. This is in accordance with earlier investigations (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ; Al-rahmi, Othman, & Yusuf, 2015c ; Barkand, 2017 ; Madjar et al., 2013 ; Salmon, 2014 ).

Discussion and implications

Developing a new hybrid technology acceptance model through combining TDT and BTT has been the major objective of the current research, which aimed to investigate the guiding factors towards utilizing online learning platforms to improve students’ academic achievements and satisfaction in higher education institutions. The current research is intensifying a step forward by implementing TDT along with a BTT model. Using the proposed model, the current research examined how students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. The current research found that students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy were influenced by students’ satisfaction. Also, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. This conclusion is consistent with earlier correlated literature. Thus, this reveals that learners first make sure whether using platforms of online learning were able to meet their study requirements, or that using platforms of online learning are relevant to their study process before considering employing such technology in their study. Learners have been noted to perceive that platforms of online learning is more useful only once they discover that such a technology is actually better than the traditional learning which does not include online learning platforms (Choy & Quek, 2016 ; Illinois Online Network, 2003 ). Using the proposed model, the current research examined how to improve students’ academic achievements and satisfaction. Thus, the following section will be a comparison between this study results and previous research, as follows.

The first hypotheses of this study demonstrated a positive and significant association between students’ prior background towards online platforms with their satisfaction. As clearly investigated in Osika and Sharp ( 2002 ) study, numerous learners deprived of these main skills enroll in the courses, struggle, and subsequently drop out. In addition, Bocchi, Eastman, and Swift ( 2004 ) investigation claimed that prior knowledge of students’ concerns, demands along with their anticipations is crucial in constructing an efficient instruction. Thus, to clarify, students must have prior knowledge and background before letting them into the online platforms. On the other hand, there are constant concerns about the online learning platforms quality in comparison to a face-to-face learning environment, as students do not have the essential skills required toward using online learning platforms (Illinois Online Network, 2003 ). Moreover, a study by Alalwan et al. ( 2019 ) discovered that Austrian learners still would rather choose face-to-face learning for communication purposes, and the preservation of interpersonal relations. This is due to the fact that learners do not as yet have the background knowledge and skills needed towards using online learning platforms. Additional research by Orton-Johnson ( 2009 ) among UK learners claimed that learners have not accepted online materials, and continue to prefer traditional context materials as the medium for their learning, which also indicates the importance of prior knowledge and background towards online platforms before going through such a technology.

The second hypotheses of this study proposed a positive and significant association between students’ experience along with students’ satisfaction, which revealed that putting the students in such an experience would provide and support them with the ability to overcome all difficulties that arise through the limits around the technical ability of the online platforms. This is in line with some earlier researches regarding the reasons that lead to people’s technology acceptance behavior. One reason is the notion of “conformity,” which means the degree to which an individual take into consideration that an innovation is consistent with their existing demands, experiences, values and practices (Chau & Hu, 2002 ; Moore & Benbasat, 1991 ; Rogers, 2003 ; Taylor & Todd, 1995 ). Moreover, (Anderson & Reed, 1998 ; Galvin, 2003 ; Lewis, 2004 ) claimed that most students who had prior experience with online education tended to exhibit positive attitudes toward online education, and it affects their attitudes toward online learning platforms.

The third hypotheses of this study demonstrated a positive and significant association among student collaboration with themselves in online platforms, which indicates the key role of collaboration between students in order to make the experiment more realistic and increase their ability to feel more involved and active. This is agreement with Al-rahmi, Othman, and Yusuf ( 2015f ) who claimed that type, quality, and amount of feedback that each student received was correlated to a student’s sense of success or course satisfaction. Moreover, Rabinovich ( 2009 ) found that all types of dialogue were important to transactional distance, which make it easier for the student to adapt to online learning platform. Also, online learning platforms enable learners to share then exchange information among their colleagues Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ).

Students’ interaction with the instructor in online platforms

The fourth hypothesis of this study proposed a positive and significant correlation between students’ collaborations and students’ satisfaction, which indicates the significance of the communication between students and their instructor throughout the online platforms experiment. These results agree with (Mathieson, 2012 ) results, which stated that the ability of communication between students and their instructor lowered the sense of separation between learner and educator. Moreover, in line with (Kassandrinou et al., 2014 ), communication guides learners to undergo constructive emotions, for example relief, satisfaction and excitement, which assist them to achieve their educational goals. In addition, (Furnborough, 2012 ) draws conclusion that learners’ feeling of cooperating with their fellow students effects their reaction concerning their collaboration with their peers. Moreover, Kassandrinou et al., 2014 focused on the instructor as crucial part as interaction and communication helpers, as they are thought to constantly foster, reassure and assist communication and interaction amongst students.

Student’s autonomy in online platforms

The fifth hypotheses of this study proposed a positive and significant relationship between student’s autonomy and online learning platforms, which indicates that students need a sense of dependence towards online platforms, which agrees with Madjar et al. ( 2013 ) who concluded that a learners’ autonomy-supportive environment provides these learners with adoption of more aims, leading to more learning achievements. Moreover, Stroet et al. ( 2013 ) found a clear positive correlation on the impacts of autonomy supportive teaching on motivation of learner. O’Donnell, Chang, and Miller ( 2013 ) also argues that autonomy is the ability of the learners to govern themselves, especially in the process of making decisions and setting their own course and taking responsibility for their own actions.

Student’s satisfaction in online platforms

The sixth hypotheses of this study proposed a positive and significant correlation between student’s satisfaction with online learning platforms, which indicates a level of acceptance by the students to adapt into online learning platforms. This is in agreement with Zhu ( 2012 ) who reported that student’s satisfaction in online platforms is a statement of confidence with the system. Moreover, Kirmizi ( 2014 ) study revealed that the predictors of the learners’ satisfaction were educator’s support, personal relevance and authentic learning, whereas the authentic learning is only the predictor of academic success. Furthermore, the findings of Bordelon ( 2013 ) stated and determined a positive correlation between both satisfaction and achievement. In addition, the results of Mahle ( 2011 ) clarified that student satisfaction occurs when it is realized that the accomplishment has met the learners’ expectations, which is then considered a short-term attitude toward the learning procedure.

Hypotheses seven, eight, nine and ten of this study proposed a positive and significant relationship between student’s academic achievements with online learning platforms, which indicates the key main role of online platform with students’ academic achievements. This agrees with Whitmer ( 2013 ) findings, which revealed that the associations between student usage of the LMS and academic achievement exposed a highly systematic relationship. In contrast, Barkand ( 2017 ) found that there is no significant difference in students’ academic achievements in utilizing online platforms regarding students’ academic achievements, which is due to the fact that academic achievement towards online learning platforms requires a certain set of skills and knowledge as mentioned in the above sections in order to make such technology a success.

The seventh hypotheses of this study proposed a positive and significant correlation between students’ application and students’ academic achievements, which indicates the major key of applying in the learning process as an effected element. This is in line with the Computer Science Teachers’ Association (CSTA) taskforce in the U. S (Computer Science Teachers’ Association (CSTA), 2011 ), where they mentioned that applying elements of computer skills is essential in all state curricula, directing to their value for improving pupils’ higher order thinking in addition to general problem-solving abilities. Moreover, Gouws, Bradshaw, and Wentworth ( 2013 ) created a theoretical framework which drawn education computational thoughts compared to cognitive levels established from Bloom’s Taxonomy of Learning Purposes. Four thinking skill levels have been utilized to assess the ‘cognitive demands’ initiated by computational concepts for instance abstraction, modelling, developing algorithms, generating automated processes. Through the iPad app, LightBot. thinking skills remained recognizing (which means recognize and recall expertise correlating to the problem); Understanding (interpret, compare besides explain the problem); whereas, applying (make use of computer skills to create a solution) then Assimilating (critically decompose and analyses the problem).

The eighth hypotheses of this study proposed a positive and significant correlation between students’ remembering and students’ academic achievements, which indicates the importance of remembering as a process of retrieving information relating to what needed to be done and/or outcome attributes) over the procedure of learning according to Bloom’s Taxonomy of Educational Objectives. Additionally, Falloon ( 2016 ) claimed that responding to data indicated the use of general thinking skills to clarify and understand steps and stages needed to complete a task (average 29%); recalling or remembering information about a task or available tools (average 13%); and discussing and understanding success criteria (average 3%).

The ninth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which indicates its significance with the academic achievements as a process of criticizing the task or the problem faced by the students into phases or activities to help understanding of how to resolve the problem. The current results agree with Falloon ( 2016 ) who demonstrated the necessity to build understanding over the thinking processes employed by students once they are engaged in their work. In addition, Falloon ( 2016 ) suggested that the purpose and nature of questioning was broader than this, with questioning of self and others being an important strategy in solution development. In many respects, the questioning for those students was not much a perspective, although more a practice, to the degree that assisted them to understand their tasks, analyze intended or developed explanations and to evaluate their outcomes.

The tenth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which reveals the importance of analysis as a process of employing general thinking besides computational knowledge in order to realize the challenges through using online platforms, in addition to predictive thinking to categorize, explore and fix any possible errors throughout the whole process. Falloon ( 2016 ) claimed that analyzing was often a collaborative procedure between pairs receiving and giving counseling from others to assist in solving complications. On the other hand, online learning platforms are highly dependent on connecting and sharing as a basic strategy that needs to be employed over all stages of online learning settings, whether between students and students, or between students and their instructor. Moreover, Falloon ( 2016 ) findings showed that Analyzing (average 17%) was present in various phases of these online students’ work, which is based on what phase they were at together with their tasks, despite the fact that most analysis was associated with students depending on themselves during online process.

Conclusion and future work

In this investigation, both transactional distance theory (TDT) and Bloom’s Taxonomy theory (BTT) have been validated in the educational context, providing further understanding towards the students’ prospective perceptions on using online learning platforms to improve students’ academic achievement and satisfaction. The contribution that the current research might have to the field of online learning platforms have been discussed and explained. Additional insights towards students’ satisfactions and students’ academic achievements have also been presented. The current research emphasizes that the incorporation of both TDT and BTT can positively influence the research outcome. The current research has determined that numerous stakeholders, for instance developers, system designers, along with institutional users of online learning platforms reasonably consider student demands and needs, then ensure that the such a system is effectively meeting their requirements and needs. Adoption among users of online learning platforms could be broadly clarified by the eleven factor features which is based on this research model. Thus, the current research suggests more investigation be carried out to examine relationships among the complexity of online learning platforms combined with technology acceptance model (TAM).

Recommendations for stakeholders of online platforms

Based on the study findings, the first recommendation would be for administrators of higher institution. In order to implement online learning, there must be more interest given to the course structure design, whereas it should be based on theories and prior literature. Moreover, instructor and course developer need to be trained and skilled to achieve online learning platforms goals. Workshops and training sessions must be given for both instructors and students to make them more familiar in order to take the most advantages of the learning management system like Moodle and LMS. The software itself is not enough for creating an online learning environment that is suitable for students and instructors. If instructors were not trained and unaware of utilizing the software (e.g. Moodle) in the class, then the quality of education imparted to students will be jeopardized. Training and assessing the class instructor and making modifications to the software could result in a good environment for the instructor and a quality education for the student. Both students’ satisfaction and academic achievements depends on their prior knowledge and experience in relation to online learning. This current research intended to investigate student satisfaction and academic achievements in relation to online learning platforms in on of the higher education in Malaysia. Future research could integrate more in relation to blended learning settings.

Availability of data and materials

All the hardcopy questionnaires, data and statistical analysis are available.

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The study involved both undergraduate and graduate students at unviersiti teknologi Malaysia (UTM), an ethical approve was taken before collecting any data from the participants

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Hassan Abuhassna, Waleed Mugahed Al-Rahmi, Noraffandy Yahya, Megat Aman Zahiri Megat Zakaria & Azlina Bt. Mohd Kosnin

Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

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Abuhassna, H., Al-Rahmi, W.M., Yahya, N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. Int J Educ Technol High Educ 17 , 38 (2020). https://doi.org/10.1186/s41239-020-00216-z

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Please note you do not have access to teaching notes, study of six online learning theories shows theories should be chosen to match institutional situations and learners' backgrounds.

Human Resource Management International Digest

ISSN : 0967-0734

Article publication date: 23 August 2021

Issue publication date: 8 September 2021

The authors assessed the following six popular online theories: Cognitivism, connectivism, heutagogy, social learning, transformative learning theories and Vygotsky’s zone of proximal development (ZPD). The theories were selected because of their relevance to improving online instruction.

Design/methodology/approach

To compare them, the authors reviewed literature on adult learning theories from the following databases: Academic Search Premier, ERIC and ProQuest. They chose the most relevant articles about each theory published between 2007 and 2017, summarized them and extracted relevant information.

The theories suggest various pointers to help course designers to improve online learning. Based on cognitivism, instructors can use media-based instruction designed especially for the working memory. Similarly, connectivism informs instructors to design instruction integrated with technology. Heutagogy also promotes the integration of technology with online learning and encourages self-directed learning. Meanwhile, social learning theory informs instructors to design group discussions and activities to foster collaboration. The other three theories - cognitivism, connectivism and heutagogy – promote the integration of technology.

Originality/value

The authors said the paper was useful as it provided a theoretical framework for adult instructors and theory designers. The paper was a follow-up to another study by the sane authors of online theories. There are also research implications. While pedagogical frameworks are well-established for online learning, studies on learner motivation would establish a wider understanding of richer design formats, the authors say.

• Social learning
• Behaviorism
• Online instruction
• Cognitivism
• Transformative learning theory

(2021), "Study of six online learning theories shows theories should be chosen to match institutional situations and learners' backgrounds", Human Resource Management International Digest , Vol. 29 No. 6, pp. 5-7. https://doi.org/10.1108/HRMID-06-2021-0144

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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Exploring the impact of the adaptive gamified assessment on learners in blended learning

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• Zhihui Zhang   ORCID: orcid.org/0000-0002-0277-4937 1 &
• Xiaomeng Huang   ORCID: orcid.org/0009-0008-6864-5821 2  

Blended learning combines online and traditional classroom instruction, aiming to optimize educational outcomes. Despite its potential, student engagement with online components remains a significant challenge. Gamification has emerged as a popular solution to bolster engagement, though its effectiveness is contested, with research yielding mixed results. This study addresses this gap by examining the impact of adaptive gamified assessments on young learners' motivation and language proficiency within a blended learning framework. Under Self-Determination Theory (SDT) and Language Assessment Principles, the study evaluates how adaptive gamified tests affect learner engagement and outcomes. A 20-week comparative experiment involved 45 elementary school participants in a blended learning environment. The experimental group ( n  = 23) took the adaptive gamified test, while the control group ( n  = 22) engaged with non-gamified e-tests. Statistical analysis using a paired t-test in SPSS revealed that the implementation of adaptive gamified testing in the blended learning setting significantly decreased learner dissatisfaction (t (44) = 10.13, p  < .001, SD = 0.87). Moreover, this approach markedly improved learners' accuracy rates (t (44) = -25.75, p  < .001, SD = 2.09), indicating enhanced language proficiency and motivation, as also reflected in the attitude scores (t(44) = -14.47, p  < .001, SD = 4.73). The adaptive gamified assessment primarily enhanced intrinsic motivation related to competence, with 69% of students in the experimental group reporting increased abilities. The findings suggest that adaptive gamified testing is an effective instructional method for fostering improved motivation and learning outcomes.

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1 Introduction

In the rapidly evolving landscape of educational technology, blended learning (BL) has become a prominent approach, seamlessly integrating face-to-face and online learning experiences (Hill & Smith, 2023 ; Rasheed et al., 2020 ). While previous research has emphasized the widespread adoption and benefits of BL, including improved academic achievement (Boelens et al., 2017 ; Hill & Smith, 2023 ), challenges faced by students, teachers, and educational institutions in its implementation are also recognized (Rasheed et al., 2020 ). Designing effective blended learning (BL) presents several key challenges, including diminished learner attention and a decline in motivation, which result in decreased engagement and participation in courses (Khaldi et al., 2023 ). Furthermore, students may face difficulties with preparatory tasks and quizzes prior to in-person classes, often due to inadequate motivation (Albiladi & Alshareef, 2019 ; Boelens et al., 2017 ).

Gamification, defined as the educational use of game mechanics and design principles extending beyond traditional games (Schell, 2008 ), has garnered attention. Studies highlight its positive impact on learning motivation, emphasizing the mediating role of psychological needs under the self-determination theory (Deci & Ryan, 2016 ). This positions gamification as a potential solution for addressing challenges in blended learning. Recent systematic research underscores the significance of leveraging gamification in online environments to enhance student engagement (Bizami et al., 2023 ; Jayawardena et al., 2021 ).

The increasing popularity of gamified tests has positively influenced academia, supporting blended learning models and formal education settings (Bolat & Taş, 2023 ; Saleem et al., 2022 ). Recent findings suggest that gamified assessments contribute to higher process satisfaction among students compared to traditional assessments (Cortés-Pérez et al., 2023 ; Sanchez et al., 2020 ). The advent of machine learning algorithms has given rise to adaptive gamified assessments, offering a novel approach to personalized testing and feedback, thereby enhancing learning autonomy (Llorens-Largo et al., n.d. ). Therefore, this study focuses on investigating the impact of gamified assessment on blended learning.

While existing research has explored the impact of gamification in online environments (Can & Dursun, 2019 ; Ramírez-Donoso et al., 2023 ), a noticeable gap remains in understanding the specific effects of gamified tests in online settings, particularly within the context of K-12 education. Research on adaptive gamified assessments is limited, emphasizing the need for further exploration (Bennani et al., 2022 ). Consequently, this study primarily focuses on investigating adaptive gamified assessments, with research objectives centered around motivation and knowledge levels in early education. The research objectives are outlined as follows:

Does adaptive gamified assessment enhance learners' motivation on blended learning? What is the effect of the adaptive gamified assessment on learners' motivation?

Does adaptive gamified assessment improve learners’ academic performance on blended learning?

To address the challenges present in blended learning, this research contributes to the field by providing insights into the effects of machine learning-based gamified assessments on motivation and performance, offering valuable recommendations for the improvement of blended learning. The findings could also facilitate the design and adoption of blended learning, particularly in the context of K-12 education.

The subsequent sections will delve into a comprehensive literature review, conceptual framework, outline the chosen methodology, present results, and discussions, and conclude with implications and avenues for future research.

2 Literature review

2.1 blended learning challenges and benefits.

Blended learning has emerged as a popular educational model, distinct from traditional instructional methods. It represents a convergence of in-person and online learning, leveraging the strengths of each to enhance the educational experience (Poon, 2013 ). The hybrid approach combines classroom effectiveness and socialization with the technological benefits of online modules, offering a compelling alternative to conventional teaching models. It has identified significant improvements in academic performance attributable to blended learning's efficiency, flexibility, and capacity (Hill & Smith, 2023 ). The approach also facilitates increased interaction between staff and students, promotes active engagement, and provides opportunities for continuous improvement (Can & Dursun, 2019 ).

Despite these advantages, blended learning is not without its challenges, particularly for students, teachers, and educational institutions during implementation. Boelens et al. ( 2017 ) highlight that students often face self-regulation challenges, including poor time management and procrastination. The degree of autonomy in blended courses requires heightened self-discipline, especially online, to mitigate learner isolation and the asynchronous nature of digital interactions (Hill & Smith, 2023 ). Isolation can be a critical issue, as students engaged in pre-class activities such as reading and assignments often do so in solitude, which can lead to a decrease in motivation and an increase in feelings of alienation (Chuang et al., 2018 ; Yang & Ogata, 2023 ).

Teachers, on the other hand, encounter obstacles in technological literacy and competency. Personalizing learning content, providing feedback, and assessing each student can demand considerable time and effort (Cuesta Medina, 2018 ; Bower et al., 2015 ). These challenges can adversely affect teachers' perceptions and attitudes towards technology (Albiladi & Alshareef, 2019 ). Furthermore, from a systems perspective, implementing Learning Management Systems (LMSs) that accommodate diverse learning styles is a significant hurdle. It necessitates a custom approach to effectively support differentiated learning trajectories (Albiladi & Alshareef, 2019 ; Boelens et al., 2017 ; Brown, 2016 ). Current research efforts are thus focused on enhancing the effectiveness of blended learning and its facilitation of independent learning practices.

2.2 Gamification in education

Gamification in education signifies the integration of game design elements into teaching activities that are not inherently game-related. This approach is distinct from game-based learning, where the primary focus is on engaging learners in gameplay to acquire knowledge. Gamification introduces game dynamics into non-gaming environments to enrich the learning experience (Alsawaier, 2018 ).

With the progression of technology, gamification has become increasingly prevalent within educational frameworks, aiming to amplify student engagement, motivation, and interactivity (Oliveira et al., 2023 ). Empirical evidence supports that gamification can effectively address issues such as the lack of motivation and frustration in educational contexts (Alt, 2023 ; Buckley & Doyle, 2016 ). Components like levels and leaderboards have been successful as external motivators, promoting a competitive spirit among learners (Mekler et al., 2017 ). Furthermore, research indicates that gamification can have enduring effects on student participation, fostering beneficial learning behaviors (Alsawaier, 2018 ).

Despite these positive aspects, some scholarly inquiries have presented a more nuanced view, suggesting that gamification does not unilaterally enhance academic outcomes. These varying results invite deeper investigation into the conditions under which gamification can truly enhance the educational experience (Oliveira et al., 2023 ). In light of such findings, recent gamified designs have increasingly emphasized personalization, taking into account the unique characteristics, needs, and preferences of each student. Studies have explored the tailoring of gamification frameworks to align with diverse student profiles (Dehghanzadeh et al., 2023 ; Ghaban & Hendley, 2019 ), learning styles (Hassan et al., 2021 ), pedagogical approaches, and knowledge structures (Oliveira et al., 2023 ). However, the literature still presents contradictory findings, and there is a relative dearth of research focusing on learning outcomes (Oliveira et al., 2023 ).

2.3 Adaptive assessment in education

Adaptive learning harnesses technological advancements to create a supportive educational environment where instructional content is tailored to individual learning processes (Muñoz et al., 2022 ). This pedagogical approach is grounded in the principle of differentiated instruction, allowing for the personalization of educational resources to meet diverse learning requirements (Reiser & Tabak, 2014 ).

Adaptive assessments, stemming from the philosophy of adaptive learning, dynamically adjust the difficulty of questions based on a learner's previous answers, terminating the assessment once enough data is collected to form a judgment (Barney & Fisher Jr, 2016 ). In the digital age, with the proliferation of e-learning, there has been a significant shift towards adaptive computer-based assessments (Muñoz et al., 2022 ), utilizing AI-based modeling techniques (Coşkun, 2023 ), and emotion-based adaptation in e-learning environments (Boughida et al., 2024 ). These assessments are characterized by their ability to modify testing parameters in response to student performance, employing machine learning algorithms to ascertain a student’s proficiency level.

Prior studies on adaptive methods have revealed several advantages, such as delivering personalized feedback promptly, forecasting academic achievement, and facilitating interactive learning support. These advantages extend to potentially enhancing learner engagement and outcomes (Muñoz et al., 2022 ). However, adapting instruction to cater to varied skill levels remains a challenge, as does addressing the issues of demotivation and anxiety among students undergoing assessment (Akhtar et al., 2023 ). Consequently, current research is concentrated on boosting student motivation and engagement in adaptive assessments.

In the field of gamified education, adaptive gamification aims to merge adaptive learning principles with game elements to enrich the learning experience. This approach has been explored through the use of data mining techniques on student logs to foster motivation within adaptive gamified web-based environments (Hassan et al., 2021 ). Despite these innovative efforts, empirical research on gamified adaptive assessment is limited, as the field is still developing.

2.4 Integration of blended learning and gamified assessment

The combination of blended learning with gamified assessment has been recognized for its potential to increase student engagement, a critical factor often lacking in online learning compared to traditional classroom settings (Dumford & Miller, 2018 ; Hill & Smith, 2023 ). Studies investigating the role of gamification within online learning environments have found that it can enhance students’ achievement by fostering greater interaction with content (Taşkın & Kılıç Çakmak, 2023 ). Moreover, gamified activities that demand active participation can promote active engagement (Özhan & Kocadere, 2020 ).

Investigations into the efficacy of Gamified Assessment in online environments suggest that students may reap the benefits of its motivational potential. For instance, research has adapted motivational formative assessment tools from massively multiplayer online role-playing games (MMORPGs) for use in cMOOCs, demonstrating positive outcomes (Danka, 2020 ). Another study compared the effects of traditional online assessment environments to those employing gamified elements, such as point systems, observing the impact on student task completion and quality in mathematics assessments (Attali & Arieli-Attali, 2015 ). Collectively, these studies indicate that gamified tests can indirectly benefit learning by enhancing the instructional content.

While many studies affirm the efficacy of gamified tests as a valuable, cost-effective tool for educators in blended learning environments (Sanchez et al., 2020 ), there is a noted gap in research addressing individual differences within gamified testing. Particularly, empirical research on adaptive gamified assessment is scarce, with more focus on the computational aspects of system development than on the impacts on motivation and academic achievement. Furthermore, while studies suggest that gamified tests may enhance the 'testing effect'—the phenomenon where testing an individual's memory improves long-term retention—most of this research is centered in higher education (Pitoyo & Asib, 2020 ).

The use of gamification spans various educational levels, from primary and secondary schooling to university and lifelong learning programs. However, research focusing on the implementation of gamification in primary and secondary education tends to prioritize the perspective of educators and the application within instructional activities (Yang & Ogata, 2023 ), rather than the online assessment itself. Therefore, this study aims to advance the empirical understanding of the application of gamification in assessments and its potential to improve learning outcomes, particularly in early education.

3 Theoretical framework

3.1 self-determination theory (sdt).

Self-Determination Theory (SDT) is a well-known theory of motivation that offers an in-depth understanding of human needs, motivation, and well-being within social and cultural environments (Chiu, 2021 ). Gamification, which applies gaming elements in non-game settings, frequently employs SDT to address educational challenges in both gamified and online learning platforms (Chapman et al., 2023 ). SDT distinguishes itself by its focus on autonomous versus controlled forms of motivation and the impact of intrinsic and extrinsic motivators, as characterized by Ryan and Deci ( 2000 ). Unlike intrinsic motivation, which is driven by internal desires, extrinsic motivation relies on external incentives such as rewards, points, or deadlines to elicit behaviors—commonly seen in the reward structures of gamified learning environments. In these adaptive gamified assessments, the provision of points and rewards for task completion serves to regulate extrinsic motivation, offering various rewards and titles each time a student completes an exercise task.

SDT is a comprehensive theory that explores the intricacies of human motivation. A subset of SDT, Cognitive Evaluation Theory, postulates that three innate psychological needs—autonomy, competence, and relatedness—propel individuals to act (Deci & Ryan, 2012 ). Autonomy is experienced when individuals feel they have control over their learning journey, making choices that align with their self-identity, such as selecting specific content areas or types of questions in an adaptive gamified assessment. Competence emerges when individuals encounter optimal challenges that match their skills, where adaptive gamified assessments can adjust in difficulty and provide feedback, thereby promoting skill acquisition and mastery. Relatedness is the sense of connection with others, fostered by supportive and engaging learning environments. In gamified contexts, this can be achieved through competitive elements and parental involvement in the learning process, enhancing the learning atmosphere.

The fulfillment of these psychological needs, particularly those of autonomy and competence, is central to fostering intrinsic motivation according to SDT. Figure  1 examines the adaptive gamified assessment process and how it aligns with SDT.

The structure of the adaptive gamified assessment

3.2 Principles of language assessment

The adaptive gamified assessment in this study utilizes Quizizz, an online educational technology platform that offers formative gamified tests to help students develop academic skills in various subjects, including English language (Göksün & Gürsoy, 2019 ). Drawing on the five principles of language assessment as outlined by Brown and Abeywickrama ( 2004 ), this study analyzes the adaptive gamified assessment. These principles—authenticity, practicality, reliability, validity, and washback—are foundational in foreign language teaching and assessment.

Practicality refers to the flexibility of the test to operate without constraints of time, resources, and technical requirements. Quizizz’s adaptive assessments are seamlessly integrated into blended learning environments, designed for time efficiency, and require minimal resources, making them suitable for a broad range of educational contexts. The platform's user-friendly design ensures that assessments are easily administered and completed by students, necessitating only an internet connection and a digital device (Göksün & Gürsoy, 2019 ).

Reliability is the extent to which an assessment consistently yields stable results over time and across different learner groups, providing dependable measures of language proficiency. Quizizz's algorithms adapt task difficulty based on learner responses, offering consistent outcomes and measuring student performance reliably over time (Munawir & Hasbi, 2021 ).

Validity concerns the assessment's ability to accurately measure language abilities in alignment with intended learning outcomes and real-world language application. Quizizz's assessments measure language skills that correlate directly with curriculum-defined learning outcomes, ensuring that results are valid representations of a student's language capabilities. The gamified context also mirrors competitive real-life scenarios, enhancing the authenticity of language use (Priyanti et al., 2019 ).

Authenticity indicates that assessments should mirror real-life language usage, providing tasks that are meaningful and indicative of actual communication situations. Quizizz's assessments incorporate tasks resembling real-world communicative scenarios, such as reading passages, interactive dialogues, and written responses that reflect authentic language use (Brown & Abeywickrama, 2004 ).

Washback refers to the influence of assessments on teaching and learning practices, which should be constructive and foster language learning. Quizizz's immediate feedback from adaptive assessments can positively affect teaching and learning. Instructors can utilize the results to pinpoint student strengths and areas for improvement, customizing their teaching strategies accordingly. Students benefit from being challenged at the appropriate level, bolstering motivation and facilitating the acquisition of new language skills in a gradual, supportive manner (Munawir & Hasbi, 2021 ).

Previous research has demonstrated that Quizizz has a significant impact on academic performance across various educational institutions (Munawir & Hasbi, 2021 ). As an exemplar of gamified adaptive assessment, Quizizz is designed to be practical and reliable while offering valid and authentic assessments of language proficiency. Moreover, it strives for a positive washback effect on the learning process, promoting effective language learning strategies and accommodating personalized education.

4 Methodology

4.1 research design.

This study employed a controlled experimental design within a quantitative research framework. The methodology involved several stages, as illustrated in Fig.  2 . Firstly, participants were selected based on their responses to a pre-questionnaire and a pre-assessment, ensuring comparable baseline levels in English proficiency and computer literacy among all participants. Subsequently, participants were randomly assigned to either the control or the experimental group to ensure variability and minimize bias. Over a period of 20 weeks, a blended language learning intervention was administered to both groups. This intervention involved accessing identical online learning resources before and after traditional classroom sessions, with equal amounts of offline instruction time. Daily assessments were conducted throughout the intervention period. The experimental group completed gamified adaptive tests via Quizizz, while the control group undertook non-gamified adaptive tests on a computer. Upon completion of the intervention, surveys were conducted to assess the motivation levels of both groups and compare their English language proficiency. Data were collected from both pre- and post-assessments, as well as responses from the questionnaires and structured interviews.

Flowchart of the experimental process for assessing the impact of gamified learning on student outcomes

4.2 Participants

Forty-five English learners from primary schools in China, aged 8 to 10 years (M = 9.40, SD = 0.62), participated in this study. The sample comprised 25 girls (55.56%) and 20 boys (44.44%). Insights into students' previous experiences, motivations for formative assessments, and attitudes toward language learning were gathered through a pre-questionnaire. Informed consent was obtained from all participants and their guardians, and confidentiality and anonymity were maintained throughout the study. Participants see in Fig.  3 were randomly divided into a control group (n = 22; 12 girls and 10 boys) and an experimental group (n = 23; 13 girls and 10 boys). The experimental group received instructions on completing and utilizing the adaptive gamified assessment, Quizizz, while the control group completed non-gamified adaptive tests on a computer. Both groups adopted the same blended learning model and were informed of identical deadlines for weekly formative assessments, requiring an accuracy rate of over 90%. Immediate feedback was provided on the accuracy rates, and participants were informed they could attempt the assessment again if the target was not met.

Comparison of number and gender ratio in two groups

4.3 Instruments

The study utilized Quizizz's Adaptive Question Bank mode, offering a range of question difficulties and allowing students to progress at their own pace. The questionnaire was adapted from the Student Evaluation of Educational Quality (SEEQ), which has demonstrated a high level of reliability, with Cronbach's alpha ranging from 0.88 to 0.97. Additionally, according to Pecheux and Derbaix ( 1999 ), the questionnaire was designed to be as concise as possible for young learners and was administered in their native language, Chinese.

The content of the questionnaire includes a 5-point Likert scale used to measure students' attitudes toward adaptive gamified tests. The response options are as follows: strongly agree = 9, agree = 7, neutral = 5, disagree = 3, and strongly disagree = 1. The statements cover various aspects of gamified testing, including Engagement and Enjoyment, exemplified by 'You enjoy learning new words through game tests. Game tests make learning grammar and vocabulary more fun for you.' Anxiety and Confidence, as indicated by 'Game tests help you feel less worried about making mistakes in learning.' Understanding and Retention, highlighted by 'Playing educational games helps you understand your lessons better.' And preference over traditional testing methods, as shown by 'You prefer taking quizzes when they are like games compared to regular tests.' This total score will provide a cumulative measure of their attitude toward gamified language tests. In addition, there are questions asking participants to express their overall satisfaction with the blended learning experience as a percentage. This metric is instrumental in assessing the role of gamified testing within the blended learning framework. Furthermore, there are specific aspects of gamification: binary yes/no questions that delve into specific components and potential effects of gamified tests, such as the impact of leaderboards and rewards on motivation, and willingness to spend extra time on gamified tests.

Moreover, to explore the impact of adaptive gamified assessment on motivation, structured interviews were conducted with the experimental group. The questions, adapted from Chiu ( 2021 ), primarily focused on aspects of motivation such as amotivation, external regulation, intrinsic motivation, and the psychological needs related to relatedness, autonomy, and competency, as seen in Table  1 . Responses were quantified on the same Likert scale, with options ranging from 'strongly agree' to 'strongly disagree.'

5 Results and discussion

5.1 comparison language learning attitude scores and satisfaction of participants.

To analyze the impact of adaptive gamified assessments on learners, the trajectory of language learning attitude scores and satisfaction percentage for two groups over the course of the experiment was explored, with results depicted in Fig. 4 and Fig. 5.

In Fig.  4 , the total score of language learning attitude for the control group's online assessment and the experimental group's adaptive gamified assessment demonstrates an increasing trend as the experiment progressed. After 4 weeks, the language learning attitude scores of the control and experimental groups were 10 and 47, respectively. By week 16, the experimental group's score increased to 70, and after 20 weeks, the control group's score was 50, while the experimental group's score reached 75. A paired-samples t-test conducted via SPSS indicated that the attitude scores were significantly higher in the experimental group than in the control group (t(44) = -14.47, p  < 0.001, SD = 4.73), as detailed in Table  2 . This significant difference in attitude scores demonstrates the effectiveness of the adaptive gamified assessment in enhancing the language learning attitude of students over the duration of the experiment.

Change of language learning attitude scores of two groups

Figure  5 reveals that as the experiment progressed, the students' dissatisfaction rates with gamification online learning decreased significantly in both groups. Initially, after 4 weeks, the average dissatisfaction rate for the control and experimental groups was 11% and 6%, respectively. As the experiment continued, the dissatisfaction rates declined, dropping to about 5% in the experimental group and 8% in the control group after 20 weeks. Paired t-test results further show a significant decrease in dissatisfaction (t(44) = 10.13, p  < 0.001, SD = 0.87). This suggests a marked downward trend in students' dissatisfaction with gamified online learning over the duration of the study, in accordance with their attitudes towards adaptive gamified assessment.

Variation curve of dissatisfaction rate of gamification in two groups

Our research found that students maintain a positive attitude towards the blended learning model of online assessment, which aligns with previous research (Abduh, 2021 ; Albiladi & Alshareef, 2019 ), indicating that e-assessment can benefit online learning and teaching. However, a deeper comparison between non-gamified and gamified adaptive testing groups in terms of satisfaction and students' subjective perceptions reveals differences. The experimental group, which incorporated gamified adaptive testing, demonstrated a more positive attitude, corroborating the positive role of gamification in education as outlined by Bolat and Taş ( 2023 ). Gamified assessment promotes student motivation in a manner consistent with previous research (Bennani et al., 2022 ), and our study has similarly shown that gamified assessment positively influences learners' behaviors and attitudes (Özhan & Kocadere, 2020 ).

This result appears to contradict the findings of Kwon and Özpolat ( 2021 ), which suggest that gamification of assessments had a significantly adverse effect on students' perceptions of satisfaction and their experience of courses in higher education. Our findings, however, indicate that adaptive gamified assessments enhance motivation and engagement, thus contributing positively to the learning process for young learners. Furthermore, the motivational levels in the experimental group remained stable, whereas motivation in the control group decreased over time. This suggests that adaptive gamified assessments may help to sustain or enhance learner motivation within a blended learning environment.

5.2 Effect of adaptive gamified assessment on learners' motivation

To further examine the effect of adaptive gamified assessments, the standard error of dissatisfaction for both groups was evaluated, while also including a statistical analysis of the distribution of motivation within the experimental group. The outcomes of these analyses are depicted in Fig.  6 .

Changing Curves of Satisfaction of Standard Errors of Two Groups

In Fig.  6 , a notable decrease in standard error scores for both the control and experimental groups is observed as the experiment progresses. Initially, after 4 weeks, the standard error scores stood at 8 for the control group and 5 for the experimental group. At the end of the 20-week study period, these scores had diminished to 5.4 and 2.8, respectively.

This study's findings are consistent with previous research on the benefits of personalization in gamification. Rodrigues et al. ( 2021 ) reported that personalized gamification mitigated negative perceptions of common assessment activities while simultaneously motivating and supporting learners. This reinforces the pivotal role of adaptive assessment in tailoring learning experiences compared to traditional e-assessment methods. Furthermore, structured interviews conducted with the experimental group revealed the distribution of students' motivation in Table  3 . For younger learners, external motivation induced by gamified testing was found to be predominant, with 73% of the children acknowledging its influence. Notably, the tests' impact on students' intrinsic motivation was also significant, especially regarding the sense of competency; 69% of students reported feeling an enhancement in their abilities. This finding presents a nuanced difference from Dahlstrøm's ( 2012 ) proposition that gamified products and services could both facilitate and undermine intrinsic motivation through supporting or neglecting the basic psychological needs for autonomy and competence. It suggests an alternate conclusion: the gamified adaptive assessment enhances intrinsic motivation and participation. Of course, the effectiveness of such interventions is significantly dependent on individual and contextual factors, thus highlighting the adaptive gamified approach's role in effectively moderating these effects.

5.3 Impact of adaptive gamified assessment on academic performance

To evaluate the impact of adaptive gamified assessment on learners’ academic performance, the errors and system score data from the model tests of different groups were organized. Figure  7 depicts the error variation of the system model test, while Fig.  8 analyzes the change curve of the system’s average score data.

Variation curves of test errors of different models in two groups

Change curve of average learning scores of learners in Two Groups

Figure  7 demonstrates that systematic errors in model testing for both groups exhibited a decreasing trend over the course of the experiment. Initially, after 4 weeks, the model test errors were 22% for the control group and 23% for the experimental group. Following 16 weeks, both groups reached a minimum test error value of 3%. However, after 20 weeks, a rebound and increasing trend in model test errors were observed in both groups. Consequently, setting the experiment duration to 16 weeks appears to effectively improve the accuracy of the gamified assessment. A paired-samples t-test in Table  4 indicates a significant reduction in standard error (t(44) = -25.75, p  < 0.001, SD = 2.09), reinforcing the effectiveness of the adaptive gamified strategy optimization in reducing learning standard errors and, consequently, improving learners' efficiency and knowledge acquisition.

As shown in Fig.  8 , the average learning scores of students in both groups increased as the experiment progressed. After four weeks, the average learning score was 25 for the control group and 48 for the experimental group. After 16 weeks, these scores increased to 36 and 66, respectively. By week 20, the average score for the experimental group slightly decreased to 63. This indicates that learners' average scores in different experimental groups peaked after 16 weeks. A comprehensive evaluation, which included a comparison of average learning scores and standard deviation (SD) changes, was used to assess the impact of the gamified assessment. The results are detailed in Table  5 .

These comparisons reveal that adaptive gamified assessments can enhance students' online learning experiences. This supports the findings of Attali and Arieli-Attali ( 2015 ), who demonstrated that under a points-gamification condition, participants, particularly eighth graders, showed higher likeability ratings. Additionally, the effect of gamified assessment on students' final scores was mediated by intrinsic motivation levels. This contrasts with previous studies on gamification in education, such as Alsawaier ( 2018 ), which indicated that students in gamified courses exhibited less motivation and lower final exam scores than those in non-gamified classes. Furthermore, the element of peer competition introduced by gamification was more meaningful to students with better results, aligning with the findings of Göksün and Gürsoy ( 2019 ). Adaptive gamified tests, serving as a formative assessment platform, have been found to positively influence young learners' learning outcomes. Moreover, gamified testing could reduce language anxiety, consistent with the study by Hong et al. ( 2022 ). Compared to traditional gamified assessments, adaptive assessments are better equipped to address issues of repetition and learner capability fit, and they align more closely with the principles of scaffolding in education, thereby enhancing students' academic performance.

6 Conclusion

This research explores the influence of adaptive gamified assessment within a blended learning context on young learners' motivation and academic performance. Grounded in Self-Determination Theory (SDT), this investigation categorizes student motivation and analyzes their engagement and learning capabilities in relation to non-gamified and gamified adaptive tests. The findings suggest that the gamified adaptive test can significantly help learners improve their motivation and foster enhanced language proficiency performance in a blended learning environment.

The study verifies the enhancing effect of gamified evaluation on the internalization of students' motivation (Özhan & Kocadere, 2020 ) and confirms the regulatory role of gamified elements in blended learning, aiding in increasing student participation and satisfaction (Jayawardena et al., 2021 ). Furthermore, the positive role of gamification in language learning and as a tool for reinforcing assessment is corroborated (Priyanti et al., 2019 ). This study extends our understanding of the motivational impacts of gamification in younger education settings, suggesting that while previous research indicated a lesser effect on intrinsic motivation for young learners (Mekler et al., 2017 ), the adaptive mode of gamified assessment could enhance students' sense of competency and, thereby, their intrinsic motivation. Additionally, this research integrates the relationship between motivation and academic level, suggesting that the transition from external motivations provided by rewards in adaptive gamified assessments to enjoying personalized feedback and growth can enhance satisfaction in blended learning, facilitating the internalization of motivation towards participation and language proficiency.

In terms of managerial and policy implications, the introduction of gamification into blended learning environments is advisable, not only as a teaching method but also as an assessment tool. Gamified assessment, with its interactive nature, can be used to alleviate negative impacts of language learning, such as anxiety and lack of confidence, especially for young learners who may benefit from guided external motivational factors. Educators should implement a variety of formative assessments using technology in evaluation activities, especially to promote active learning.

However, the short duration of the experiment and the limited sample size are insufficient to substantiate long-term positive effects of gamification. Future research should delve into a more nuanced examination of students' intrinsic motivation, with longitudinal tracking to observe the internalization of motivation. The inclusion of delayed tests can help study the long-term effects of gamification. Further research could also compare adaptive gamified experiments with gamified experiments to enhance understanding of how gamification influences the internalization of students' intrinsic motivation.

Data availability

All data and questionnaires seen in the attachment.

Data will be available on request due to privacy/ethical restrictions.

Code availability

Not Applicable.

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Students’ Sense of Belonging Matters: Evidence from Three Studies

On Thursday, February 16, we hosted Dr. Maithreyi Gopalan to discuss her latest research on how students’ sense of belonging matters.

• Evidence has shown that in certain contexts, a student’s sense of belonging improves academic outcomes, increases continuing enrollment, and is protective for mental health. In some of the studies presented, these correlations were still present beyond the time frame of the analysis, suggesting that belonging might have a longitudinal effect.
• Providing a more adaptive interpretation of challenge seemed to help students in a belonging intervention make alternative and more adaptive attributions for their struggles, forestalling a potential negative impact on their sense of belonging.

Professor Gopalan began her talk by discussing how the need for “a sense of belonging” has been identified as a universal and fundamental human motivation in the field of psychology. John Bowlby, one of the first to conduct formal scientific research on belonging, examined the effects on children who had been separated from their parents during WWII (Baumeister & Leary, 1995). From his pioneering work, Bowlby and colleagues proposed that humans are driven to form lasting and meaningful interpersonal relationships, and the inability to meet this need results in loneliness and mental distress. Educational psychologists adapted the concept of belonging to indicate how students’ sense of fit with themselves and with their academic context can affect how they perceive whether they can thrive within it (Eccles & Midgley, 1989; Eccles & Roeser, 2011).

After providing this brief overview of what belonging means more broadly, Dr. Gopalan introduced the concept of “belonging uncertainty” pioneered by social psychologists Geoffrey Cohen and Gregory Walton at Stanford University (Walton & Cohen, 2007) to describe the uncertainty students might feel about their belonging when entering a new social and academic situation , which is most pronounced during times of transition (e.g., entering college). Research has shown that belonging uncertainty affects how students make sense of daily adversities, often interpreting negative events as evidence for why they do not belong. Belonging uncertainty may result in disengagement and poor academic outcomes. In contrast, a sense of belonging is associated with academic achievement, persistence in the course, major, and college (Walton & Cohen, 20011, Yeager & Walton, 2011). It is the concept of belonging uncertainty that is the focus of Dr. Gopalan’s presentation, with emphasis on the findings from the following key research questions:

• How do students’ sense of belonging in the first year correlate with academic persistence and outcomes at a national level?
• Can belonging interventions during the first semester of college lead to increased persistence and academic achievement in a diverse educational setting?
• How does a student’s sense of belonging amidst the COVID-19 pandemic correlate with mental health?

Study 1: College Students’ Sense of Belonging: A National Perspective (Gopalan & Brady, 2019)

Most research examining college students’ sense of belonging has come from studies looking at one or a few single four-year institutions. To examine how belonging differs across student identities and institutions, Professor Gopalan and colleagues looked at the responses from the only nationally representative survey of college students to date that had measured belonging. The Beginning Postsecondary Students Longitudinal Study (BPS) (Dudley et al ., 2020) sampled first-time beginning college students from 4070 eligible two- and four-year institutions (N= 23, 750 students), surveyed during their first year and subsequently two years later.

Professor Gopalan examined average measurements of belonging across institution type and student characteristics (Gopalan & Brady, 2019) and associations between belonging measurements and measurements of academic achievement, including GPA and persistence (continued enrollment), self-reported mental health, and self-reported use of campus services. The results, Dr. Gopalan explained, were striking: underrepresented racial and ethnic minority students (URMs) and first-generation/low-income students (FGLIs) reported a lower sense of belonging in four-year colleges than their non-URM and non-FGLI counterparts. 1 Importantly, they also found that having a greater sense of belonging is associated with higher academic performance, persistence, and is protective for mental health in year three of students’ undergraduate trajectory, suggesting that belonging might have a longitudinal effect (Gopalan & Brady, 2019). These findings were consistent with previous results from smaller studies involving single institutions. Sense of belonging is important not just in specific institutions but nationally, and social identity and context matter . One practical and policy-driven takeaway from this study is that only one national data set currently measures students’ sense of belonging using a single item. More robust measurements and large data sets might reveal additional insights into the importance of belonging for students’ educational experiences.

1 At two-year colleges, first-year belonging is not associated with persistence, engagement, or mental health. This suggests that belonging may function differently in two-year settings. More work is ongoing to try to understand the context that might be driving the difference. (Deil-Amen, 2011).

Study 2: A customized belonging intervention improves retention of socially disadvantaged students at a broad-access university (Murphy et al ., 2020)

Professor Gopalan and colleagues wanted to understand how to adapt existing belonging interventions to different educational contexts and dig deeper into underlying psychological processes underpinning belonging uncertainty. Because previous social-belonging interventions were conducted in well-resourced private or public institutions, Professor Gopalan was interested in examining whether the positive effects of belonging interventions could be extended to a broader-access context (context matters as not all extensions of belonging interventions have been shown to reproduce persistent changes in enrollment and academic outcomes). For this purpose, the traditional belonging interventions were customized for a four-year, Hispanic-serving public university with an 85% commuter enrollment using focus groups and surveys. Based on prior research, belonging interventions provide an adaptive lay theory for why students encounter challenges during transition times (Yeager et al ., 2016). Students, particularly those with little knowledge of how college works or those who have experienced discrimination, or are aware of negative stereotypes about their social group, may make global interpretations of why college can be challenging and may even associate challenges as evidence that they and students like them don’t belong. With belonging interventions, the lay theory provided to students aims to frame the experience of challenge in more adaptive ways—challenge and adversity are typical experiences, particularly during transitional moments, and should be expected; adapting academically and socially takes time—students will be more likely to persist, seek out campus resources and develop social relationships.

• They acknowledge that challenges are expected during transitions and that these are varied.
• They communicate to students that most students, including students from non-minority groups, experience similar challenges and feelings about them.
• They communicate that belonging is a process that takes time and tends to increase over time
• They use student examples of challenges and resolutions.

The Intervention

All students in the first-year writing class were randomly assigned to either the belonging group or an active control group. The intervention was provided to first-year students in their writing class and consisted of a reading and writing assignment about social and academic belonging. The control group was given the same assignment but with a different topic, study skills. In the intervention group, students read several stories from a racially diverse set of upper-level students who reflected on the challenges of making friends and adjusting to a new academic context. The hypothetical students reflected on the strategies they used, the resources they accessed, and how the challenge dissipated over time. After the reading exercise, the students in the intervention group were instructed to write about how the readings echoed their own first-year experiences. Then, they were asked to write a letter to future students who might question their belonging during their transition to college. Research has shown that written reflections help students internalize the main messages of the belonging intervention (Yeager & Walton, 2011).

Similar to previously published belonging interventions, results in persistence and academic achievement were significant for minoritized groups in the belonging cohort:

• Persistence. Compared to the control group, continuous enrollment for URM & FGLI students increased by 10% one year after and 9% two years after the intervention.
• Performance. The non-cumulative GPA from the URM & FGLI students increased by 0.19 points the semester immediately following the intervention and by 0.11 over the next two years compared to students in the control group.

Immediately following the intervention, a selected sub-sample of students in both conditions was invited to take a daily diary survey for nine consecutive days. The daily diary survey assessed students’ daily positive and negative academic and social experiences (students were asked to report and describe three negative and three positive events that they faced daily and to rate how positive and negative the events were), as well as their daily sense of social and academic belonging. The daily-diary assignment revealed another interesting finding: the intervention did not change the overall perception of negative events. URM & FGLI students in both groups had a statistically similar daily-adversity index and reported the same number of daily adverse events on average. However, there was no connection between the adversity index and sense of belonging for students in the belonging cohort. In contrast, students in the control group evidenced a negative correlation between daily adversities and belonging: “the greater adversity disadvantaged students experienced on a day, the lower their sense of social and academic fit” (Murphy et al ., 2020).

Providing a more adaptive interpretation of challenge seemed to help students in the belonging condition make alternative and more adaptive attributions for their struggles that did not connect to their sense of belonging. A follow-up survey one year after the intervention showed that minoritized students in the belonging intervention continued to report a higher sense of belonging in comparison to their counterparts in the control group.

Study 3: College Student’s Sense of Belonging and Mental Health Amidst the COVID-19 Pandemic (Gopalan et al ., 2022)

Dr. Gopalan presented the third study, which turned out to provide a unique opportunity to assess whether sense of belonging had predictive effects on mental health. In the fall of 2019, researchers sent a survey to students at a large, multicampus Northeastern public university called the College Relationship and Experience survey (CORE), which included two questions about belonging, among other items. In the Spring of 2020, after students were sent home due to the COVID-19 pandemic, a variation of the same survey was sent to students who had taken the CORE survey. After controlling for pre-COVID depression and anxiety, Dr. Gopolan and colleagues found that students who reported a higher sense of belonging in the fall of 2019 had lower rates of depression and anxiety midst-COVID pandemic , with the effects on depression more strongly predictive than those for anxiety. The correlation between a lower sense of belonging and higher rates of depression and anxiety was also found to be strongest for first-year students, who had little time during their first year to build community and adjust to college before the pandemic hit.

Dr. Gopalan concluded with some practical advice for instructors: “Stop telling students they belong, show them instead that they belong,” citing a recent op-ed from Greg Walton . We do this by modeling the idea that belonging is a process that takes time and by communicating to students that they are not alone , which can be done through sharing our own experiences with belonging, and by allowing students space to hear the experiences of their peers and learn from one another.

• Classroom Practices Library which includes Overview: Effective Social Belonging Messages are more.
• The Project for Education Research That Scales (PERTS) : a free belonging intervention for four-year colleges and universities.
• Research library on belonging
• Article on Structures for Belonging: A Synthesis of Research on Belonging-Supportive Learning Environments
• “Stop telling students ‘You Belong!’”
• Everyone is talking about belonging: What does it really mean?
• Post-secondary
• Academic Belonging : introduction to the concept and practices that support it.
• Flipping Failure : a campus-wide initiative to help students feel less alone by hearing stories about how their peers coped with academic challenges

Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117 (3), 497–529. https://doi.org/10.1037/0033-2909.117.3.497

Deil-Amen, R. (2011). Socio-academic integrative moments: Rethinking academic and social integration among two-year college students in career-related programs. The Journal of Higher Education , 82(1), 54-91. https://doi.org/10.1080/00221546.2011.11779085  

Dudley, K., Caperton, S.A., and Smith Ritchie, N. (2020). 2012 Beginning Postsecondary Students Longitudinal Study (BPS:12) Student Records Collection Research Data File Documentation (NCES 2021-524). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved 2/27/2023 from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid-2021524

Eccles, J. S., & Midgley, C. (1989). Stage/Environment Fit: Developmentally Appropriate Classrooms for Early Adolescence. In R. E. Ames, & Ames, C. (Eds.), Research on Motivation in Education , 3, 139-186. New York: Academic Press.

Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21 (1), 225–241. https://doi.org/10.1111/j.1532-7795.2010.00725.x

Gopalan, M., & Brady, S. T. (2020). College Students’ Sense of Belonging: A National Perspective. Educational Researcher , 49(2), 134–137. https://doi.org/10.3102/0013189X19897622

Gopalan, M., Linden-Carmichael, A. Lanza, S. (2022). College Students’ Sense of Belonging and Mental Health Amidst the COVID-19 Pandemic, Journal of Adolescent Health , 70(2), 228-233. https://doi.org/10.1016/j.jadohealth.2021.10.010

Murphy, M.C., Gopalan, M., Carter, E. R., Emerson, K. T. U., Bottoms, B. L., and Walton, G.M., (2020). A customized belonging intervention improves retention of socially disadvantaged students at a broad-access university Science Advances, 6(29). DOI: 10.1126/sciadv.aba4677

Walton, & Cohen. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82. https://doi.org/10.1037/0022-3514.92.1.82

Walton, G.M., & Cohen, G.L. (2011). A Brief Social-Belonging Intervention Improves Academic and Health Outcomes of Minority Students. Science,  331(6023), 1447-1451.  DOI: 10.1126/science.1198364

Yeager, D. S., & Walton, G. M. (2011). Social-Psychological Interventions in Education They’re Not Magic. Review of Educational Research, 81(2), 267–301. http://doi.org/10.3102/0034654311405999

Yeager, D.S., Walton G.M., Brady, S.T., Dweck, C.S.,(2016). Teaching a lay theory before college narrows achievement gaps at scale, Psychological and Cognitive Sciences , 113(24), E3341-E3348. https://doi.org/10.1073/pnas.1524360113

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Liberty University’s online  Doctor of Education in Educational Leadership  degree is designed to train you on best practices in education and technology to help you enhance student learning, become a competent administrator, and best serve your students and their parents. This program focuses on translating classroom theory into real-world application and is designed to provide you with the skills and knowledge you need to effectively lead and manage in an educational environment.

Higher Education Administration – Assessment and Evaluation

Liberty University’s online  Doctor of Education in Higher Education Administration – Assessment and Evaluation  degree covers research-based approaches to analyzing student learning, curricula, academic programs, student services, and institutional structures. In this program, you’ll explore how to help colleges and universities maintain high standards of quality and accreditation so that their students receive the best education possible.

Higher Education Administration – Educational Leadership

Liberty University’s online  Doctor of Education in Higher Education Administration – Educational Leadership  degree can help you strengthen your leadership skills and advance your career in postsecondary education administration. You will analyze the major challenges facing today’s educational leaders and explore methods for helping colleges and universities thrive.

Higher Education Administration – Organizational Leadership

Liberty University’s online  Doctor of Education in Higher Education Administration – Organizational Leadership  degree is designed to provide training in university administration and advanced business topics. You can develop essential skills in business management and use your business knowledge to help your college or education-related organization thrive.

Higher Education Administration – Student Affairs

Liberty University’s online  Doctor of Education in Higher Education Administration – Student Affairs  degree can help you learn how to support college students’ personal, professional, and academic development. That way, you can help them develop essential skills for navigating challenges in school – and in life.

Instructional Design and Technology

Liberty University’s online Doctor of Education in  Instructional Design and Technology  investigates the intersection of education, technology, and creativity. You can gain the necessary knowledge and skills to become a leader in designing and implementing effective educational strategies using cutting-edge technology.

Special Education

Liberty University’s online  Doctor of Education in Special Education  degree can help you learn how to manage and construct educational programs geared for those students with additional educational needs. You can have a profound impact on the lives of your students with the concepts you can master in this program.

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• A non-refundable, non-transferable $50 application fee will be posted on the current application upon enrollment (waived for qualifying service members, veterans, and military spouses – documentation verifying military status is required) .
• Send official college transcripts (mailed as sealed, unopened copies or sent via a direct electronic transcript system). A regionally or nationally accredited master’s degree with at least a 3.0 GPA is required for admission in good standing.
• Applicants must submit a copy of their current initial state teaching or school counseling license to  [email protected] .
• Applicants whose native language is other than English must submit official scores for the Test of English as a Foreign Language (TOEFL) or an approved alternative assessment. For information on alternative assessments or TOEFL waivers, please call Admissions or view the official International Admissions policy .

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If you are sending in a preliminary transcript for acceptance, you must:

• Be in your final term and planning to start your doctoral degree after the last day of class for your master’s degree.
• Complete a Master’s Self-Certification Form confirming your completion date. You may download the form from the Forms and Downloads page or contact an admissions counselor to submit the form on your behalf.
• Submit an official transcript to confirm that you are in your final term. The preliminary transcript must show that you are within 6 credit hours of completion for a 30-48 credit hour master’s degree or within 9 credit hours of completion for a 49+ credit hour master’s degree.
• Send in an additional, final official transcript with a conferral date on it by the end of your first semester of enrollment in the new doctoral degree.

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An acceptable official college transcript is one that has been issued directly from the institution and is in a sealed envelope. If you have one in your possession, it must meet the same requirements. If your previous institution offers electronic official transcript processing, they can send the document directly to [email protected] .

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Liberty University is dedicated to providing world-class educational experiences to military students across the globe.

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Available Benefits:

• Tuition discounts – $275 per credit hour for EdS and EdD courses
• Additional discount for veterans who service in a civilian capacity as a First Responder (less than $625 per course)
• 8-week courses, 8 different start dates each year, and no set login times (may exclude certain courses such as practicums, internships, or field experiences)

Frequently Asked Questions

Why should you get a doctorate in education.

Through Liberty University’s online EdD programs, you will have the opportunity to engage with knowledgeable education professionals who want to mentor and help you to become an advanced educator, administrator, and researcher. Our doctorate in education offers a robust, theoretical framework that can advance your teaching methods and theories to a higher order of application, allowing you to evaluate and address teaching challenges in K-12 institutions and at the university level.

Is a Doctorate in Education worth it?

Yes – we think so! You can be better prepared to take advantage of the increased market demand for leaders in education

• With an online doctorate in education, you may be  eligible for higher pay  than those who hold a master’s degree alone.
• Throughout the program, you can network with your peers and professors while developing your writing and research for the final capstone project phase.
• Completing an online EdD gives you a terminal educational credential that shows employers you have reached the pinnacle of your field’s achievement.

What can Liberty’s doctoral programs do for you?

Liberty University’s doctoral programs can offer you advanced academic training and practical skills that align with your professional aspirations and personal values. These programs are designed to enhance your expertise in a specific field, such as education, business, or divinity, enabling you to contribute to academic knowledge, apply practical solutions in real-world settings, and ascend to higher positions in your career.

You can benefit from flexible learning options, including online courses that allow you to balance your studies with personal and professional responsibilities. Liberty’s commitment to integrating ethical and faith-based principles into its curriculum can also provide you with a unique perspective that is valued in many professional settings. Additionally, you’ll have opportunities to engage with experienced faculty and like-minded peers, expanding your professional network and gaining support that can be crucial for both academic and career advancement.

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April 30, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

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Should online educational platforms offer courses following a schedule or release them on demand?

by Marilyn Stone, American Marketing Association

Researchers from Carnegie Mellon University and University of Pennsylvania have published a new Journal of Marketing study that examines online educational platforms and the question of whether they should release content through a scheduled format that resembles a traditional university course or use an on-demand release strategy.

The study is titled "More Likely to Pay but Less Engaged: The Effects of Switching Online Courses from Scheduled to On-Demand Release on User Behavior" and is authored by Joy Lu, Eric T. Bradlow, and J. Wesley Hutchinson.

In 2011, the online education industry catered to around 300,000 consumers. In 2021, it served 220 million, thanks in part to increased enrollment during the COVID-19 pandemic. Traditional universities and institutions are increasingly adopting hybrid course formats. For example, the number of full-time online MBA students surpassed in-person MBA students for the first time in the 2020-21 academic year.

Today, online educational platforms like Coursera and edX offer a range of flexible course content, but these firms are faced with a tricky question: Should they release content through a scheduled format that resembles a traditional university course with a subset of lectures and quizzes available at the start of each week, or should they follow in the footsteps of Netflix and Hulu with an on-demand release strategy where all the material is immediately available upon registration?

This new article finds that the choice of format for content release not only impacts overall user engagement and firm revenue but also user performance and learning outcomes.

The researchers studied over 67,000 users taking an introductory marketing course on Coursera consisting of 32 short lecture videos and four quizzes. The study took advantage of a natural experiment policy change where the platform switched the course from a scheduled format to an on-demand release format while keeping the actual content the same.

The scheduled format closely resembled a traditional university course, with some of the study material available at the beginning of each week for four weeks. In the on-demand format, all four weeks of content was made available upon registration. All users could take the course for free or opt into paying for a completion certificate, either as a one-time fee in the scheduled format or a monthly subscription in the on-demand format.

More users, less engagement

The study's findings show that the switch to on-demand content doubled the percentage of paying users from 14% to 28%. Lu explains that "the on-demand format was successful in increasing short-term firm revenue by bringing in more paying users. On the downside, the switch resulted in significantly lower lecture completion rates and lower quiz performance."

The on-demand format also negatively impacted downstream platform engagement. The marketing course was promoted in a "Business Foundations" set with three other courses on operations, accounting, and finance.

"Compared to users in the scheduled format, those in the on-demand format ended up taking one or two fewer additional courses six months after the focal marketing course," says Bradlow.

Analysis of user activity reveals two new learning patterns:

• A subset (13%) of users in the on-demand format continued to return and take quizzes well beyond the recommended four-week course period. The greater flexibility in the on-demand content release and payment structure likely enabled these users to "stretch out" their consumption.
• The on-demand format increased the practice of binging—with user activity being clumped together (i.e., more binging) as compared to being evenly spaced out (i.e., less binging). In the scheduled format, binging was negatively related to course performance, which is consistent with the intuition that binging reflects procrastination or cramming. However, in the on-demand format, binging was positively related to performance, suggesting that on-demand users may binge as a form of strategic time management by setting aside time to consume in spurts.

Real-world implications

This study offers vital lessons for chief marketing officers in the online education space:

• The switch to the on-demand format attracted a set of users who were more likely to pay, but were less engaged in the course. On-demand content is potentially helpful at bringing in a new user segment or expanding the current user base, similar to universities offering concurrent hybrid MBAs that cater to busy students with full-time jobs. Managers must consider the trade-off between offering structure versus flexibility and may even consider offering different content release options simultaneously but at different price points by emphasizing their unique features.
• Platforms may need to adapt their content to account for users who binge on content and others who space it out over time. For example, firms can include more recaps or reviews to reduce frustration resulting from users forgetting content. It may even be a viable strategy to embrace the prevalence of binging among users by highlighting or designing sets of lectures that are "bingeable" versus more modular.
• Many online platforms offer episodic content that may be released in installments and thus need to make decisions regarding the content release format.

"Our study provides insights that help managers anticipate the potential consequences of such decisions," says Hutchinson. "On-demand content offers clear short-term benefits in terms of increased revenue but potentially long-term costs in terms of decreased engagement and new challenges in maintaining user engagement."

Journal information: Journal of Marketing

Provided by American Marketing Association

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