{"title":"A Bayesian Meta-Analysis of Digital Game-Enhanced Vocabulary Learning","authors":"Sofiya Shahiwala, D. R. Rahul","doi":"10.1002/jcal.70246","DOIUrl":"https://doi.org/10.1002/jcal.70246","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Digital game-enhanced vocabulary learning (DGEVL), referring to the use of commercial off-the-shelf games for vocabulary learning, has attracted growing scholarly interest. Although existing studies predominantly report positive effects, previous reviews have conflated different game types, restricted their scope or overlooked the processes that mediate learning outcomes. As a result, there is limited understanding of how and under what conditions DGEVL works, leaving gaps in theory and research practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aimed to examine the effectiveness of digital game-enhanced vocabulary learning and identify the factors that facilitate or hinder its impact.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A meta-analysis of 12 studies (14 samples, 765 participants) was conducted using a Bayesian random-effects model to assess the overall impact of DGEVL. Subgroup analyses explored the influence of study, participant, intervention and assessment characteristics. To contextualise these findings, a systematic review of 25 studies, comprising 12 quantitative studies included in the meta-analysis and an additional 13 qualitative studies, was carried out, and a thematic analysis was conducted to construct a conceptual model of the learning process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>DGEVL demonstrates a strong positive effect on vocabulary learning (posterior median <i>g</i><sub>within</sub> = 1.11, 95% credible interval (CI) [0.62, 1.61]; <i>g</i><sub>between</sub> = 1.40, 95% CI [0.58, 2.25]), with substantial between-study heterogeneity (<i>τ</i><sub>within</sub> = 0.56, 95% CI [0.26, 1.00]; <i>τ</i><sub>between</sub> <i>=</i> 0.80, 95% CI [0.26, 1.68]). These effects vary depending on factors such as the length of the intervention, learner proficiency, and the type of game played. The cyclical model conceptualises vocabulary learning as a cyclical process shaped by gameplay, language interactions and metacognitive regulation. The study highlights the need for balanced assessment techniques, prolonged interventions, micro-longitudinal data collection, comparative designs and rigorous trials.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 3","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring Shared Monitoring in Large Language Model (LLM)-Supported Online Collaborative Problem Solving for High-Cohesion and Low-Cohesion Groups","authors":"Xiaoyun Liu, Xu Du, Jui-Long Hung, Hao Li, Shuoqiu Yang, Yiqian Xie","doi":"10.1002/jcal.70249","DOIUrl":"https://doi.org/10.1002/jcal.70249","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent research increasingly highlights the central role of interventions in enhancing shared monitoring during collaborative problem-solving. However, traditional intervention approaches suffer from limitations in timeliness and adaptability. Large language model (LLM), equipped with deep semantic parsing and contextual perception, can dynamically detect latent challenges and provide targeted, timely, context-sensitive feedback.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study examines how LLM-supported interventions affect group shared monitoring during dynamic CPS processes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study designed a collaborative problem-solving platform integrated with LLM, and 28 students from a university in China participated in CPS activities. Chi-square tests, conditional random fields, linear mixed models and correlation analyses were adopted to examine the changes in both monitoring behaviour and equality of monitoring participation in high-cohesion (HCGs) and low-cohesion groups (LCGs) after LLM-supported group metacognitive scaffolding (LLM-GMS) intervention, as well as their effects on collaborative performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The results show that (1) LLM-GMS activated more socio-cognitive and behavioural monitoring in HCGs, whereas LCGs mainly exhibited heightened behavioural monitoring. (2) Descriptive analyses revealed divergent trends in monitoring participation equality across group types, with HCGs showing increased equality and LCGs exhibiting a decline. (3) In HCGs, socio-emotional monitoring was positively associated with collaborative performance, whereas participation equality and behavioural monitoring exhibited negative associations with collaborative performance. In contrast, among LCGs, behavioural monitoring was positively related to performance, whereas socio-cognitive monitoring was unexpectedly negatively associated with performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>These findings highlight that LLM-GMS can be a valuable tool for supporting collaborative learning, but its effectiveness depends on group characteristics and its implementation approach.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 3","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing Emotional Intelligence Through Generative AI-Supported Digital Storytelling: A Mixed Methods Study Using Epistemic Network Analysis","authors":"Gwo-Jen Hwang, Yun-Fang Tu, Yue-I Lin, Hsiu-Ling Chen","doi":"10.1002/jcal.70245","DOIUrl":"https://doi.org/10.1002/jcal.70245","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Social–emotional learning (SEL) has gained increasing attention in recent years due to its importance for students' emotional regulation, motivation and learning engagement. Digital storytelling (DST) has been widely recognised as a promising approach for supporting SEL; however, students often encounter challenges related to creative expression and technical execution during the storytelling process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Although DST holds considerable promise for supporting SEL, students often encounter challenges related to creative expression when using DST, which may limit their learning effectiveness. To address this issue, this study proposed a generative-AI (GAI)-supported DST approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A quasi-experimental design was adopted with 62 junior high school students. The experimental group (<i>n</i> = 30) learned using the GAI-DST approach, while the control group (<i>n</i> = 32) adopted a conventional DST approach. Quantitative data were collected through pre- and post-questionnaires on emotional intelligence and self-efficacy. Qualitative interview data were further analysed using epistemic network analysis (ENA) to explore students' learning perceptions and cognitive-emotional patterns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Results showed that the GAI-DST approach significantly enhanced students' self-efficacy and the motivation dimension of emotional intelligence, while no significant differences were found in other emotional intelligence dimensions. The qualitative findings revealed that students in the GAI-DST group demonstrated a stronger orientation towards practice-based learning, tool-supported problem solving and emotional engagement, whereas students in the control group focused more on mastering basic skills. These findings suggest that the GAI-DST approach may exert selective effects on motivational and self-efficacy-related processes, rather than producing immediate, broad-based improvements across all dimensions of emotional intelligence. By conceptualising GAI as a mediational learning tool that supports mastery experiences and reflective interaction, this study provides a theoretically grounded explanation of how GAI can support SEL and offers suggestions for the design of AI-integrated instructional activities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 3","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147708165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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