enhancing vocabulary research paper

The design of technology-enhanced vocabulary learning: A systematic review

• Published: 15 January 2024

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• Keyi Zhou 1 ,
• Fangzhou Jin 1 ,
• Weiwei Li 2 ,
• Zicong Song 1 ,
• Xianhan Huang 3 &
• Chin-Hsi Lin   ORCID: orcid.org/0000-0002-4730-0671 1  

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Some meta-analyses have confirmed the efficacy of technology-enhanced vocabulary learning. However, they have not delved into the specific ways in which technology-based activities facilitate vocabulary acquisition, or into first-language vocabulary learning. We conducted a systematic review that retrieved 1,221 journal articles published between 2011 and 2023, of which 40 met our inclusion criteria. Most of the sampled studies focused on teaching receptive vocabulary knowledge and vocabulary breadth. All utilized cognitive strategies. Their common design features included noticing and receptive or productive retrieval, and most implicitly drew upon dual-coding theory. Our findings highlight the need for a balanced approach to vocabulary learning, encompassing both vocabulary breadth and depth, as well as receptive and productive knowledge. They also suggest that affective and social learning strategies should be promoted alongside the cognitive ones that are currently dominant. Additionally, our identification of commonly and rarely used design features can guide curriculum designers to develop more effective tools. Lastly, we argue that the design of technology-enhanced learning should be theory-driven.

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Introduction: A Scoping Review of Second Language Vocabulary Learning in the Wild

Current Perspectives on Vocabulary Teaching and Learning

Data availability

This review article does not contain any new data as it is a comprehensive analysis and synthesis of existing literature. The findings and conclusions presented in this review are based on the analysis and interpretation of publicly available information, including published articles, books, and other documented sources. All the references cited in this review are provided to ensure transparency and enable readers to access the relevant sources for further exploration. No additional datasets were generated or analyzed during the completion of this review.

(Note: References preceded by an asterisk are the articles included in this review)

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Implications for Practice

What is already known about this topic:

• Vocabulary knowledge is essential for language learning and reading comprehension.

• Technology-enhanced vocabulary learning has been proven effective.

What this paper adds:

• Most studies focus on teaching receptive vocabulary knowledge and vocabulary breadth.

• Cognitive strategies were used in all studies.

• Noticing and receptive or productive retrieval were common design features.

Implications for theory, policy, or practice:

• Teachers should balance the focus on vocabulary breadth and depth, and receptive and productive knowledge.

• Affective, social, and metacognitive strategies should be promoted.

• Stakeholders can use the identified design features to choose the most appropriate tools for their needs.

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Zhou, K., Jin, F., Li, W. et al. The design of technology-enhanced vocabulary learning: A systematic review. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-023-12423-y

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DOI : https://doi.org/10.1007/s10639-023-12423-y

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This study's goal was to contrast the results of of various vocabulary teaching methods, such as using context, dictionary definitions, and synonyms to teach vocabulary, on both transient and persistent vocabulary memory. Three groups of 90 intermediate EFL students were chosen at random and divided into: context, definition, and synonyms (thirty learners in each group). They received instruction in 30 vocabulary words over the course of two sessions. Two post-tests were administered, the first four weeks after the lesson and the second eight weeks later, to gauge student retention of vocabulary words. One-way ANOVA was used to evaluate the results. When compared to the other two approaches, the context method outperformed them in both postponed tests, according to the data. The study's conclusions have pedagogical ramifications for EFL instructors, students, syllabus designers, content creators, and language test creators. The study's findings also offer useful details ...

Journal of Language …

Saeed Ketabi

Hadia Hakem

Vocabulary learning is one of the major challenges learners face in foreign language learning process. To allay this burden, EFL teachers may teach their learners how to learn it. Teachers, then, and within the learner-centred approach may train their learners to use memory strategies to help them tackle the task of vocabulary learning efficiently and independently; and with willingness and motivation from the part of the learners they can enrich their lexicon and even become more autonomous. The present paper describes an experiment which was led with second-year university students to check the efficacy of teaching these strategies. Results showed that memory strategies, characterized as deep strategies, ensure a better retention of the learnt vocabulary and lead to more efficient learner autonomy. And above this, it gave students more self-confidence and motivation. But prior to this, the related literature review is presented about vocabulary and its importance in foreign langua...

Colombian Applied Linguistics Journal

Roberto Alvira Quiroga

The present study reports on an action research study that explores the implications of applying three vocabulary strategies: word cards, association with pictures, and association with a topic through fables in the acquisition of new vocabulary in a group of EFL low-level proficiency teenagers in a public school in Espinal, Tolima, Colombia. The participants had never used vocabulary strategies before and struggled to memorize and recall words. Two types of questionnaires, a researcher’s journal, and vocabulary tests were the instruments used to gather data. The results showed that these strategies were effective to expand the range of words progressively and improve the ability to recall them. The study also found that these strategies involve cognitive and affective factors that can affect students’ perception about the strategies and their use. The implementation of the strategies highlighted the need to train teachers and learners in strategies intended to teach and learn voc...

yanna queencer

Rina Rachmawati, M.Pd

Rachmawati, Rina. 2012. Junior High School Students’ Vocabulary Learning Strategies. Thesis. Program Studi Bahasa dan Sastra. Program Pascasarjana Universitas Negeri Surabaya. Supervisors: (I) Prof. Dr. Susanto, M.Pd., and (II) Prof. Dr. Soekemi, M. A. Key words: Vocabulary learning strategies, Determination strategies, Social strategies, Cognitive strategies, Metacognitive strategies. In learning a new language, the first thing language learners usually do is to understand the meaning of new words. Vocabulary mastery is an important element to understand a new language. Knowing that vocabulary is an important thing in a language, the efforts to help learners to master vocabulary has become crucial. One of the ways to help learners to master vocabulary is that by the help of learning strategies. Some students, especially junior high school students, sometimes find the difficulty to express their ideas in written or spoken. They also have difficulties in understanding the text books written in English. To overcome these problems, most of junior high school students have tried many strategies such as by asking teacher for L1 translation, making vocabulary lists, and using bilingual dictionaries. This ways are able to help them to acquire new words that they find in English text books. Based on the previous reasons, the researcher conducted a research to describe the types of learning strategies that is employed by the Junior high school students. This research attempts to answer some research questions, those are: (1) what types of strategies do high achievers of junior high school students’ employ in their attempt to master vocabulary?, (2) What types of strategies do average achievers of junior high school students’ employ in their attempt to master vocabulary?, (3) What types of strategies do low achievers of junior high school students’ employ in their attempt to master vocabulary?. Related to the research questions, the researcher used descriptive qualitative approach. The researcher did the research to the first grade students of junior high school students at SMP Jati Agung, Sidoarjo who divided into 3 groups (higher achievers, average achievers, lower achievers). The data of this research were students’ verbal and non-verbal behaviors while using vocabulary learning strategies in their learning process. To collect the data, the researcher used observation and interview. The observations were done four times and the interview was done once after the class was over. After collecting the data, the researcher transcribed and analyzed them. The result shows that high achievers employed four strategies in their attempt to master English vocabulary. Those strategies are determination strategies, social strategies, cognitive strategies, and metacognitive strategies. Average achievers employed three strategies: determination strategies, social strategies, and metacognitive strategies. And low achievers employed three strategies in their attempt to master English vocabulary. Those are determination strategies, social strategies, and metacognitive strategies.

Theory and Practice in Language Studies

Mohammad Reza Ghorbani

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Enhancing Vocabulary Memorization and Retention through LMS and MultiEx Game Platforms among Thai Tertiary Students

Although technology has been integrated into vocabulary instruction, to date, few studies have compared whether learning management system (LMS) vocabulary exercises or vocabulary online games facilitate better vocabulary acquisition. The purpose of this study was to investigate whether the Test of English for International Communication (TOEIC) vocabulary lessons plus LMS exercises and TOEIC vocabulary lessons plus MultiEx games (online games) foster short-term vocabulary memorization and long-term vocabulary retention, as well as which performed better. Participants were 72 first-year students at a university in southern Thailand. They were divided into two experimental groups, one given LMS exercises and the other MultiEx games. A word list was taken from the TOEIC word list and a pre-test was used to determine how many words students knew. The unknown words were used in the design of the vocabulary lessons. Ten lessons were provided for the students. Immediately after each lesson, a post-test was conducted to measure their vocabulary recognition. Two weeks after the final lesson, a delayed post-test was conducted to determine how many of the new words had been retained. The main finding was that both vocabulary memorization and retention were enhanced through the use of LMS exercises and the use of MultiEx games. The results showed a higher mean score for the MultiEx game group in both the immediate post-tests and the delayed post-test. Although the differences between the two groups were not statistically significant, the findings suggest integrating technology enhances vocabulary learning outcomes.

https://doi.org/10.26803/ijlter.20.10.10

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Computer Science > Computation and Language

Title: researchagent: iterative research idea generation over scientific literature with large language models.

Abstract: Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts. To enhance its productivity, we propose a ResearchAgent, a large language model-powered research idea writing agent, which automatically generates problems, methods, and experiment designs while iteratively refining them based on scientific literature. Specifically, starting with a core paper as the primary focus to generate ideas, our ResearchAgent is augmented not only with relevant publications through connecting information over an academic graph but also entities retrieved from an entity-centric knowledge store based on their underlying concepts, mined and shared across numerous papers. In addition, mirroring the human approach to iteratively improving ideas with peer discussions, we leverage multiple ReviewingAgents that provide reviews and feedback iteratively. Further, they are instantiated with human preference-aligned large language models whose criteria for evaluation are derived from actual human judgments. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines, showcasing its effectiveness in generating novel, clear, and valid research ideas based on human and model-based evaluation results.

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I previously wrote about the importance of attracting public attention to scientific research . In today’s world, where billions of people are attached to their digital devices watching the very addictive but often useless TikTok content or receiving instant gratification by engaging in meaningless debates about celebrities, scientists need to find creative ways to have their research noticed. Popularizing scientific research helps inspire the younger generations to go into science and provide the general public with a sense of optimism enabling the government to channel more resources into science. People do need inspiration. But very often, even very important scientific breakthroughs requiring many years, hard work, skill, funding, and genuine serendipity go largely unnoticed by the general public.

One of the best ways to measure expert and public attention is the cumulative Altmetric Attention Score , originally developed by Digital Science and adopted by many prestigious publishers, including Nature Publishing Group. Every Nature paper and the papers published by pretty much every credible publisher are tracked by Digital Science by the Document Object Identification (DOI) or the Unique Resource Locator (URL) . While Altmetric has many limitations, for example, it does not track LinkedIn posts and may not adequately cover the impact of top-tier media coverage, at the moment it is the blueprint for tracking attention.

Altmetric Score in The Age of Generative AI

Media attention is likely to be very important in the age of generative AI. Many modern generative systems, such as ChatGPT, Claude, Mistral, and Gemini, as well as hundreds of Large Language Models (LLMs) in China, use the data from the same sources referenced in Altmetric to learn. The more times generative systems see the same concept presented in different contexts, the better they learn. So if you want to contribute to the training of AI systems that may thank you for it in the future - Altmetric is the way to go.

So what can a research group do to ensure they are communicating their findings effectively and increasing the visibility of their research to ensure it gets reflected in the Altmetric Attention Score?

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Altmetric openly discloses the weights of the various sources and the scoring algorithm is relatively straightforward. It is easy to learn, and there are multiple online resources providing advice on how to share your research in ways that will be captured by Altmetric. Cambridge University Press published a guideline to Altmetric for the authors on how to popularize their research with Altmetric in mind. Wolters Kluwer put out a guide and the editor of Toxicology and Pathology wrote a comprehensive overview of Altmetric and how to use it. Surprisingly, this overview got an Altmetric Attention Score of only 4 at the time of the writing, but was cited 137 times according to Google Scholar .

Altmetric monitors social networks, including X (formerly Twitter), Facebook, and Reddit; all major top-tier mainstream media, mainstream science blogs, policy documents, patents, Wikipedia articles, peer review websites, F1000, Syllabi, X (formerly Twitter), tracked Facebook pages, Reddit, one of the Stack Exchange sites, and Youtube. Unfortunately, several powerful platforms, including LinkedIn, are not currently tracked.

The popularity of the paper depends on many factors. Firstly, it has to be novel, trendy, and newsworthy. You are unlikely to get high Altmetric Score with a boring topic. Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, consistently get higher coverage.

Screenshot of the Altmetric Attention Score "Flower" showing several tracked sources

Here are the five tips for increasing the visibility of your work and ensuring that reach is tracked and reflected by Altmetric:

1. Understand How Altmetric System Works

Congratulations, if you read this article and looked at what sources are tracked by Altmetric. Most likely, you got the basics and will be able to get a “balanced flower” by making a press release, tweeting the DOI of the paper on X, posting a video overview of your paper on Youtube, announcing on Reddit (I still need to learn how to do this).

To understand how Altmetric works, I emailed a few questions to Miguel Garcia, Director of Product and Data Analytics Hub at Digital Science and my first question was wether the Altmetric algorithm is open source. “The Altmetric Attention Score's calculation is not open source but we try to provide as much information as possible around how we calculate it here, and are currently considering what steps we might take to make our algorithms more transparent.” He also provided a link to how the Altmetric Attention Score is calculated.

Many professionals use LinkedIn as the primary social media resource and I was wondering why Altmetric stopped tracking it. Bad news - technical reasons prevent tracking DOIs on LinkedIn. Good news - they are actively seeking ways to appropriately track mentions on LinkedIn and we may see some news toward the end of the year.

My other big question was how does Altmetric count tweets and retweets on X. What if there are many posts from the same account? Miguel’s response was: “Re-tweets count less than original tweets. In addition to that, modifiers are applied to the type of account that is tweeting in order to reduce the weight of the tweet in situations where we find signals of bias or promiscuity (for example a journal publisher only tweeting their own articles). Besides that, we have conditions around the maximum number of retweets in order to limit the maximum impact they would have.”

So tweeting the article many times will not help you. But if other scientists tweet you paper with a DOI - these tweets will get counted. So tweet others as you would like to be tweeted.

2. Make a Press Release and Distribute to Science-focused Media

If your paper is significant, for example, you elucidated novel disease biology, discovered a new drug, developed a new fancy algorithm, designed a new material, or developed a new application for a quantum computer, it is worthwhile investing some time and resources in writing a press release. If you are working for an academic institution, most likely they have a communications office that will help you. If you do not have this luxury, you will need to learn how to write a press release. Plenty of free online guides cover the basics of press release writing. And press releases are one area where ChatGPT and other generative tools do surprisingly well. Upload your paper and ask it to write a press release, check for errors or exaggerations, edit, and you are ready to go. Just make sure to include the DOI and the URL of your paper. A proper business press release on BusinessWire or PRNewswire may cost several thousand dollars. In my opinion, these resources are dramatically overcharging while providing little service. I don't remember a case where a journalist picked up our news based on a commercial press release. But these releases are often reposted by other online press release distributors and the boost to Altmetric may be considerable. The default news release distribution service for research news is EurekAlert. This resource may sometimes result in journalistic coverage as many reporters are using it for science news. There are many free resources you can use if you do not have any budget.

Once the press release is issued, share it with the media. Share the resulting news coverage via your social networks and contacts. Many journalists track the popularity of their news articles and giving them several thousand extra views from professional audience and increasing their social following increases the chances that they will cover the next important research paper.

3. Make a Blog Post

Writing a blog post can be longer and more comprehensive than the press release. Make sure to add fancy diagrams and graphical explainers. You can share the blog post with the journalists at the same time as the press release. Your blog may serve as a source of inspiration for third party news coverage. Make sure to reference the DOI and URL of your paper.

If your paper is in one of the Nature journals, consider writing a “Behind the Paper" blog post on Nature Bioengineering Community. Surprisingly, these blogs are rarely picked up by Altmetric but may serve as a source of inspiration for the journalists and social media influencers. Plus, it is a resource by the Nature Publishing Group.

4. Tweet and Ask Your Team Members to Tweet

Each post on X gives you a quarter of an Altmetric point. If your paper goes viral on X, your Altmetric score can be considerable. Plus, once journalists notice that it went viral, they will be more likely to cover the story, further increasing the score.

Try to choose the time of the post, the hashtags, and the images wisely. Since Elon Musk took over X and opened the algorithm it became very transparent and easy to optimize for. Here are the top 10 tips for boosting attention for a post on X. Make sure to include the DOI or the URL of the paper for Altmetric to find the post.

5. Experiment, Learn, Repeat

My highest Altmetric Attention Score core to date was around 1,500 for a paper in Nature Biotechnology published in 2019, where we used a novel method for designing small molecules called Generative Tensorial Reinforcement Learning (GENTRL) to generate new molecules with druglike properties that got synthesized and tested all the way into mice. In 2024, we went further and showed that an AI-generated molecule for an AI-discovered target was tested all the way up to Phase II human trials, but the paper published in Nature Biotechnology, let’s call it the TNIK paper , has achieved a score ofjust over 600 to date. So what has changed and what can we learn from these two papers?

The popularity of the paper depends on many factors. Ones which capture the public imagination or have widespread appeal are of course, much more likely to gain traction online. When we published the GENTRL paper in 2019, Generative AI was in its infancy, and there are pretty much no other companies that I heard of at the intersection of generative AI and drug discovery. We also published multiple articles in this field in the years leading to that paper and many key opinion leaders (KOLs) followed us. That following included a small army of generative AI skeptics who not only contributed to multiple rejections in peer-reviewed journals but also openly criticized this approach in social networks. This criticism also helped boost the Altmetric Score and bring more attention to the study. So first learning from this exercise - negative publicity helps overall publicity. As long as you are certain that your research results are honest - leave room for criticism and even help expose your paper’s weaknesses. Critics are your greatest Altmetric boosters. Since readers and, by extension journalists, react to negative news and drama stronger than to positive news, critical reviews will boost your Altmetric as long as the DOI or URL of the paper is properly referenced.

Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, always get a higher level of coverage. For example, the AlphaFold paper published in July 2021 in Nature got an Altmetric Attention Score of over 3,500 . Even though I have not seen any drugs out of AlphaFold reaching preclinical candidate status, I predict the popularity of this tool will result in the first Nobel Prize in this area. Therefore, in order to become famous and popularize your research more effectively, it is a good idea to build up the public profile of yourself and your work. For example, Kardashians are famous for being famous .

Be careful with Wikipedia. I made a mistake explaining the importance of Wikipedia to students when lecturing on the future of generative AI, and one or two of them got banned for expanding the articles with paper references. Wikipedia requires that these are added by independent editors rather than the authors of papers themselves, but if some editors do not like it, they will not go deep or investigate - they will assume wrongdoing. So it is better to avoid even talking about Wikipedia. References there should happen naturally and often some of the more popular papers get picked up and referenced by veteran editors.

Experimenting with Altmetric will also help you explore new strategies for popularizing scientific research and develop new strategies for inspiring people to learn or even get into the new exciting field. UNESCO estimates that there was just over 8 million full-time equivalent (FTE) researchers in 2018 globally. Only a fraction of these are in biotechnology - less than 0.01% of the global population. If you motivate a million students to go into biotechnology by popularizing your research, you double this number.

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