The integration of pedagogical agents (PAs) into educational settings has become widespread, yet the impact of humorous versus non-humorous PAs on student academic performance and engagement remains underexplored. Although research highlights the benefits of PAs, the specific role of humour in enhancing educational outcomes is not well studied.
Objectives
This study evaluates the effectiveness of humorous PAs compared to non-humorous PAs in improving academic performance and student engagement among fifth-grade students in science education.
Methods
A quasi-experimental design with pre-test and post-test measures was used. A sample of 78 fifth-grade students from District 4 of Tehran during the 2022–2023 academic year participated in the study. Three classes, balanced by the school based on academic performance, were randomly selected, and each was assigned to one of three groups: humorous PA (n = 28), non-humorous PA (n = 25) and no PA (n = 25). Data were collected using a researcher-developed learning test and the Reeve Student Engagement Questionnaire (2013), and analysed using ANCOVA.
Results
The humorous PA group showed higher levels of student academic performance and engagement outcomes compared to the non-humorous PA and no PA groups. The ‘humorous PA’ group achieved the highest scores in both engagement and academic performance.
Conclusions
These findings suggest that incorporating humour into PAs can significantly enhance educational experiences and effectiveness. The study highlights the importance of considering the personality and style of PAs to optimise student engagement and academic performance.
{"title":"The Impact of Humorous Pedagogical Agents on Student Engagement and Academic Performance in Science Education","authors":"Fateme Ashrafzade, Yousef Mahdavinasab, Nasrin Mohammadhasani, Mahsa Moradi","doi":"10.1111/jcal.70107","DOIUrl":"https://doi.org/10.1111/jcal.70107","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The integration of pedagogical agents (PAs) into educational settings has become widespread, yet the impact of humorous versus non-humorous PAs on student academic performance and engagement remains underexplored. Although research highlights the benefits of PAs, the specific role of humour in enhancing educational outcomes is not well studied.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study evaluates the effectiveness of humorous PAs compared to non-humorous PAs in improving academic performance and student engagement among fifth-grade students in science education.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A quasi-experimental design with pre-test and post-test measures was used. A sample of 78 fifth-grade students from District 4 of Tehran during the 2022–2023 academic year participated in the study. Three classes, balanced by the school based on academic performance, were randomly selected, and each was assigned to one of three groups: humorous PA (<i>n</i> = 28), non-humorous PA (<i>n</i> = 25) and no PA (<i>n</i> = 25). Data were collected using a researcher-developed learning test and the Reeve Student Engagement Questionnaire (2013), and analysed using ANCOVA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The humorous PA group showed higher levels of student academic performance and engagement outcomes compared to the non-humorous PA and no PA groups. The ‘humorous PA’ group achieved the highest scores in both engagement and academic performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings suggest that incorporating humour into PAs can significantly enhance educational experiences and effectiveness. The study highlights the importance of considering the personality and style of PAs to optimise student engagement and academic performance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929768","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}
<div> <section> <h3> Background</h3> <p>With the rapid advancement of technology, the integration of Generative Artificial Intelligence (GAI) in education has gained considerable attention. Many studies have examined GAI's impact on learning outcomes, yet their conclusions are inconsistent, highlighting the need for a comprehensive review to clarify its overall effects and identify influential factors.</p> </section> <section> <h3> Objectives</h3> <p>This study aims to conduct a meta-analysis of the effects of GAI on student learning outcomes across cognitive, competency and affective dimensions. Additionally, it seeks to explore how various moderating factors, including subject discipline, instructional duration, knowledge type, prior knowledge and tool type, influence GAI's effectiveness.</p> </section> <section> <h3> Methods</h3> <p>A meta-analysis was performed on 34 experimental and quasi-experimental studies published internationally. Effect sizes were calculated for overall learning outcomes and categorised by dimension. Further analysis was conducted to assess the influence of moderating variables on the impact of GAI.</p> </section> <section> <h3> Results</h3> <p>The meta-analysis indicates that Generative Artificial Intelligence has a significant positive impact on overall learning outcomes, with a combined effect size of 0.68 (<i>p</i> < 0.001). The impact is particularly pronounced in the cognitive dimension (<i>g</i> = 0.795) and the competency dimension (<i>g</i> = 0.711), while its effect on the affective dimension (<i>g</i> = 0.507) is moderate but still significant. The analysis of moderating variables reveals that the effectiveness of GAI is influenced by discipline type but is not significantly affected by instructional period, knowledge type, prior knowledge level, or tool type. Specifically, GAI exhibits the highest positive effects in mathematics, science and humanities, whereas its impact is relatively lower yet still significant in computer science and medical/nursing education. Additionally, GAI's effectiveness does not significantly differ across various instructional periods, different knowledge types, learners with varying prior knowledge levels, or different AI tool versions.</p> </section> <section> <h3> Conclusions</h3> <p>To optimise GAI's use in education, the study suggests aligning GAI with specific subject needs, adapting tools for different student levels, integrating GAI with traditional teaching and establishing monitoring mechanisms. These strategies aim to maximise GAI's positive impact on learning efficienc
{"title":"A Meta-Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes","authors":"Nan Ma, Zhiyong Zhong","doi":"10.1111/jcal.70117","DOIUrl":"https://doi.org/10.1111/jcal.70117","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>With the rapid advancement of technology, the integration of Generative Artificial Intelligence (GAI) in education has gained considerable attention. Many studies have examined GAI's impact on learning outcomes, yet their conclusions are inconsistent, highlighting the need for a comprehensive review to clarify its overall effects and identify influential factors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aims to conduct a meta-analysis of the effects of GAI on student learning outcomes across cognitive, competency and affective dimensions. Additionally, it seeks to explore how various moderating factors, including subject discipline, instructional duration, knowledge type, prior knowledge and tool type, influence GAI's effectiveness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A meta-analysis was performed on 34 experimental and quasi-experimental studies published internationally. Effect sizes were calculated for overall learning outcomes and categorised by dimension. Further analysis was conducted to assess the influence of moderating variables on the impact of GAI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The meta-analysis indicates that Generative Artificial Intelligence has a significant positive impact on overall learning outcomes, with a combined effect size of 0.68 (<i>p</i> < 0.001). The impact is particularly pronounced in the cognitive dimension (<i>g</i> = 0.795) and the competency dimension (<i>g</i> = 0.711), while its effect on the affective dimension (<i>g</i> = 0.507) is moderate but still significant. The analysis of moderating variables reveals that the effectiveness of GAI is influenced by discipline type but is not significantly affected by instructional period, knowledge type, prior knowledge level, or tool type. Specifically, GAI exhibits the highest positive effects in mathematics, science and humanities, whereas its impact is relatively lower yet still significant in computer science and medical/nursing education. Additionally, GAI's effectiveness does not significantly differ across various instructional periods, different knowledge types, learners with varying prior knowledge levels, or different AI tool versions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>To optimise GAI's use in education, the study suggests aligning GAI with specific subject needs, adapting tools for different student levels, integrating GAI with traditional teaching and establishing monitoring mechanisms. These strategies aim to maximise GAI's positive impact on learning efficienc","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927271","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}
Daniel Kangwa, Mgambi Msambwa Msafiri, Antony Fute
Background
This study explored the factors that influence the balance between academic integrity and the effective use of GenAI tools in higher education. It focused on the role of institutional guidelines in enhancing the responsible use of GenAI technologies to enhance academic integrity.
Objectives
The study was theoretically grounded in the Technology Acceptance Model and the Theory of Planned Behaviour to investigate the factors that promote academic integrity in using GenAI tools (RQ1), their impact and institutional strategies to effectively mitigate ethical risks (RQ2) and the model practices to support the ethical and effective use in higher education (RQ3).
Methods
The PRISMA framework was used to systematically review and thematically synthesise the results of 213 peer-reviewed articles published between January 2021 and May 2025.
Results
Finding indicates that academic support, defined by structured training, technical scaffolding, and perceived usefulness, is critical to enabling ethical GenAI use. Additionally, student self-regulation, as influenced by behavioural control and goal setting, was associated with greater academic integrity in GenAI-mediated learning. Whereas institutional policies varied widely, those with transparent, adaptive and discipline-responsive governance frameworks more effectively mitigated academic misconduct. Indeed, the model practices included GenAI ethics committees, interactive GenAI literacy modules, and the developer-educator collaborations to promote algorithmic transparency.
Conclusions
A comprehensive systems-based approach that encompasses academic support, self-regulation and ethical guidelines is critical for the responsible use of GenAI tools in education. Hence, to preserve academic integrity while nurturing innovation, institutions should integrate GenAI ethics into curricular design, faculty development and cross-sectoral policy frameworks. Future research may expand into multilingual and longitudinal analyses to support equitable and sustainable GenAI integration across diverse educational settings.
本研究探讨了影响高等教育中学术诚信与GenAI工具有效使用之间平衡的因素。它侧重于机构准则在加强负责任地使用基因人工智能技术以加强学术诚信方面的作用。本研究以技术接受模型（Technology Acceptance Model）和计划行为理论（Theory of Planned behavior）为理论基础，探讨在使用GenAI工具时促进学术诚信的因素（RQ1）、它们的影响和有效降低伦理风险的制度策略（RQ2），以及支持伦理和有效地在高等教育中使用的模型实践（RQ3）。方法采用PRISMA框架对2021年1月至2025年5月发表的213篇同行评议文章进行系统综述和专题综合。研究结果表明，学术支持（由结构化培训、技术脚手架和感知有用性定义）对于伦理地使用GenAI至关重要。此外，受行为控制和目标设定影响的学生自我调节与基因人工智能介导的学习中更大的学术诚信有关。虽然机构政策差异很大，但那些具有透明、适应性强和符合学科的治理框架的机构更有效地减轻了学术不端行为。实际上，模型实践包括GenAI伦理委员会、交互式GenAI识字模块，以及促进算法透明度的开发者-教育者合作。一种全面的基于系统的方法，包括学术支持、自我监管和道德准则，对于在教育中负责任地使用GenAI工具至关重要。因此，为了在培育创新的同时保持学术诚信，各院校应将基因伦理纳入课程设计、师资发展和跨部门政策框架。未来的研究可能会扩展到多语言和纵向分析，以支持在不同教育环境中公平和可持续的GenAI整合。
{"title":"Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review","authors":"Daniel Kangwa, Mgambi Msambwa Msafiri, Antony Fute","doi":"10.1111/jcal.70109","DOIUrl":"https://doi.org/10.1111/jcal.70109","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study explored the factors that influence the balance between academic integrity and the effective use of GenAI tools in higher education. It focused on the role of institutional guidelines in enhancing the responsible use of GenAI technologies to enhance academic integrity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The study was theoretically grounded in the Technology Acceptance Model and the Theory of Planned Behaviour to investigate the factors that promote academic integrity in using GenAI tools (RQ1), their impact and institutional strategies to effectively mitigate ethical risks (RQ2) and the model practices to support the ethical and effective use in higher education (RQ3).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The PRISMA framework was used to systematically review and thematically synthesise the results of 213 peer-reviewed articles published between January 2021 and May 2025.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Finding indicates that academic support, defined by structured training, technical scaffolding, and perceived usefulness, is critical to enabling ethical GenAI use. Additionally, student self-regulation, as influenced by behavioural control and goal setting, was associated with greater academic integrity in GenAI-mediated learning. Whereas institutional policies varied widely, those with transparent, adaptive and discipline-responsive governance frameworks more effectively mitigated academic misconduct. Indeed, the model practices included GenAI ethics committees, interactive GenAI literacy modules, and the developer-educator collaborations to promote algorithmic transparency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A comprehensive systems-based approach that encompasses academic support, self-regulation and ethical guidelines is critical for the responsible use of GenAI tools in education. Hence, to preserve academic integrity while nurturing innovation, institutions should integrate GenAI ethics into curricular design, faculty development and cross-sectoral policy frameworks. Future research may expand into multilingual and longitudinal analyses to support equitable and sustainable GenAI integration across diverse educational settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923790","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}
Computer-assisted educational technologies that integrate affective supports into math practices could be particularly beneficial for addressing troubling declines in middle grade students' math achievement, affect, and motivation. MathSpring is a web-based intelligent tutor that offers personalised content, remedial tutoring, and affective support for students and diagnostic reports on students' progress and effort for teachers.
Objectives
This preregistered randomised control trial experiment sought to experimentally test the efficacy of the MathSpring platform in improving students' math achievement and dispositions towards math.
Methods
The final sample included 53 teachers and their 2003 10–12-year-old students from one U.S. state. Teachers were randomly assigned to either use MathSpring with their students (treatment condition) or continue business-as-usual math instruction (control condition) for one school year. Before and after the intervention, students' math achievement was measured with standardised math assessments and their dispositions towards math were measured with standardised surveys. Teachers completed logs, surveys, and interviews about their implementation.
Results and Conclusions
The results indicated that students in the treatment group did not show evidence of improved achievement, affect, or dispositions towards math compared with students who received business-as-usual math instruction. Still, exploratory analyses indicated that students with high usage demonstrated greater achievement than those in the business-as-usual group. Though no overall effects of this intervention were detected, educational technology interventions with affective supports still hold potential for improving students' academic and dispositional outcomes.
{"title":"Evaluating the Efficacy of an Intelligent Tutoring System That Integrates Affective Supports Into Math Learning","authors":"Mingyu Feng, Natalie Brezack, Chunwei Huang, Yvonne Kao, Kelly Collins, Melissa Lee, Megan Schneider, Wynnie Chan","doi":"10.1111/jcal.70106","DOIUrl":"https://doi.org/10.1111/jcal.70106","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Computer-assisted educational technologies that integrate affective supports into math practices could be particularly beneficial for addressing troubling declines in middle grade students' math achievement, affect, and motivation. MathSpring is a web-based intelligent tutor that offers personalised content, remedial tutoring, and affective support for students and diagnostic reports on students' progress and effort for teachers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This preregistered randomised control trial experiment sought to experimentally test the efficacy of the MathSpring platform in improving students' math achievement and dispositions towards math.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The final sample included 53 teachers and their 2003 10–12-year-old students from one U.S. state. Teachers were randomly assigned to either use MathSpring with their students (treatment condition) or continue business-as-usual math instruction (control condition) for one school year. Before and after the intervention, students' math achievement was measured with standardised math assessments and their dispositions towards math were measured with standardised surveys. Teachers completed logs, surveys, and interviews about their implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The results indicated that students in the treatment group did not show evidence of improved achievement, affect, or dispositions towards math compared with students who received business-as-usual math instruction. Still, exploratory analyses indicated that students with high usage demonstrated greater achievement than those in the business-as-usual group. Though no overall effects of this intervention were detected, educational technology interventions with affective supports still hold potential for improving students' academic and dispositional outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915301","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}
Computational thinking (CT) is a fundamental ability required of individuals in the 21st-century digital world. Past studies show that generative artificial intelligence (GenAI) can enhance students' CT skills. However, GenAI may produce inaccurate output, and students who rely too much on AI may learn little and be unable to think independently. Besides, most research on CT mainly focused on Scratch or programming classes, but incorporating it into the K-12 science curriculum is better for students' deep learning and CT core skills development.
Objectives
This study proposed a causal explanation and reflection (CER) model-based GenAI learning system in science courses to cultivate students' CT core skills.
Sample
One hundred and eighteen elementary school students in three different classes participated in this study.
Methods
A quasi-experiment was conducted in Fujian, China. Students in the experimental group learned with the CER model-based GenAI learning system; students learned with the CER model-based learning system in control group 1; students in control group 2 used the causal-explanation-based GenAI learning system. Students' learning achievement and CT core skills were examined.
Results
The results showed that the CER model-based GenAI learning system significantly improved students' science learning and CT core skills. Interview results further showed some students complained that GenAI only provided answers without encouraging them to comprehend the material.
Conclusions
CT should not exist only in computer courses. Instead, it is an approach to problem-solving that applies to all disciplines. Also, over-reliance on GenAI may hinder learning ability. The effectiveness of GenAI-based learning depends on its judicious use.
{"title":"Exploring the Effects of the CER Model-Based GenAI Learning System to Cultivate Elementary School Students' Computational Thinking Core Skills in Science Courses","authors":"Jia-Hua Zhao, Shu-Tao Shangguan, Ying Wang","doi":"10.1111/jcal.70110","DOIUrl":"https://doi.org/10.1111/jcal.70110","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Computational thinking (CT) is a fundamental ability required of individuals in the 21st-century digital world. Past studies show that generative artificial intelligence (GenAI) can enhance students' CT skills. However, GenAI may produce inaccurate output, and students who rely too much on AI may learn little and be unable to think independently. Besides, most research on CT mainly focused on Scratch or programming classes, but incorporating it into the K-12 science curriculum is better for students' deep learning and CT core skills development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study proposed a causal explanation and reflection (CER) model-based GenAI learning system in science courses to cultivate students' CT core skills.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Sample</h3>\u0000 \u0000 <p>One hundred and eighteen elementary school students in three different classes participated in this study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A quasi-experiment was conducted in Fujian, China. Students in the experimental group learned with the CER model-based GenAI learning system; students learned with the CER model-based learning system in control group 1; students in control group 2 used the causal-explanation-based GenAI learning system. Students' learning achievement and CT core skills were examined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results showed that the CER model-based GenAI learning system significantly improved students' science learning and CT core skills. Interview results further showed some students complained that GenAI only provided answers without encouraging them to comprehend the material.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>CT should not exist only in computer courses. Instead, it is an approach to problem-solving that applies to all disciplines. Also, over-reliance on GenAI may hinder learning ability. The effectiveness of GenAI-based learning depends on its judicious use.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918846","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}
Online collaborative learning has been broadly applied in the field of higher education. Nevertheless, not all types of collaborative learning can produce the desired learning results.
Objectives
To facilitate online collaborative learning, the present study proposed an innovative artificial intelligence-enabled group cognitive diagnosis approach with the goal of improving online collaborative learning.
Methods
A total of 135 college students was included in the current study and divided into 45 groups. A total of 15 groups consisting of 45 students used the group cognitive diagnosis approach. An additional 15 groups were assigned to the group knowledge graph approach, while the remaining 15 groups were assigned to the traditional online collaborative learning approach.
Results and Conclusions
The findings of this research indicated that the group cognitive diagnosis approach had more significant and positive impacts on collaborative learning performance, knowledge elaboration, and higher-order cognitive engagement than did the group knowledge graph and traditional online collaborative learning approaches.
Implications
The current study deepens our understanding of group cognition and the corresponding complex interactions and provides a new method for improving online collaborative learning.
{"title":"An Artificial Intelligence-Enabled Group Cognitive Diagnosis Approach With the Goal of Promoting Online Collaborative Learning","authors":"Lanqin Zheng, Zichen Huang, Lei Gao, Yunchao Fan","doi":"10.1111/jcal.70113","DOIUrl":"https://doi.org/10.1111/jcal.70113","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Online collaborative learning has been broadly applied in the field of higher education. Nevertheless, not all types of collaborative learning can produce the desired learning results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>To facilitate online collaborative learning, the present study proposed an innovative artificial intelligence-enabled group cognitive diagnosis approach with the goal of improving online collaborative learning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 135 college students was included in the current study and divided into 45 groups. A total of 15 groups consisting of 45 students used the group cognitive diagnosis approach. An additional 15 groups were assigned to the group knowledge graph approach, while the remaining 15 groups were assigned to the traditional online collaborative learning approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The findings of this research indicated that the group cognitive diagnosis approach had more significant and positive impacts on collaborative learning performance, knowledge elaboration, and higher-order cognitive engagement than did the group knowledge graph and traditional online collaborative learning approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>The current study deepens our understanding of group cognition and the corresponding complex interactions and provides a new method for improving online collaborative learning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915302","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}
Roberta Di Palma, Simon Beausaert, Dominik Mahr, Jonas Heller, Tim Hilken
Background
Despite the recognised potential of Virtual Reality (VR) in education, the role of VR in enhancing presentation skills remains uncertain. Mixed findings, coupled with low adoption rates in educational settings, highlight the need to investigate how current VR applications are designed to facilitate effective learning outcomes for students.
Objectives
Grounded in constructivist and situated learning theories, which emphasise learning through active engagement and real-world contexts, this study examines how specific VR design features, namely, feedback mechanisms (feedback awareness and feedback usefulness) and presence types (social and spatial presence), relate to student motivation and performance in presentations.
Methods
In a one-group pre-test-post-test field study, 285 university students participated in 30-min individual VR training sessions focused on presentation skills refinement. Using structural equation modelling, this study assessed the relationships between feedback and presence elements with student motivation and performance outcomes.
Results and Conclusions
Analysis revealed that feedback awareness and social presence were positively associated with students' motivation to refine their presentation skills. Effective feedback mechanisms, particularly those that enhance feedback awareness and usefulness, were crucial for skill transfer, while increased social presence was associated with improved academic performance in the classroom. Conversely, spatial presence was unexpectedly negatively related to both motivation and performance, suggesting that how VR creates spatial elements may influence learning outcomes. By examining the nuances of these VR features, this study offers valuable insights for software developers and educators aiming to improve university students' presentation skills through VR training applications.
{"title":"Does Using Virtual Reality to Enhance Students' Presentation Skills Work? The Role of Feedback and Presence","authors":"Roberta Di Palma, Simon Beausaert, Dominik Mahr, Jonas Heller, Tim Hilken","doi":"10.1111/jcal.70097","DOIUrl":"https://doi.org/10.1111/jcal.70097","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Despite the recognised potential of Virtual Reality (VR) in education, the role of VR in enhancing presentation skills remains uncertain. Mixed findings, coupled with low adoption rates in educational settings, highlight the need to investigate how current VR applications are designed to facilitate effective learning outcomes for students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Grounded in constructivist and situated learning theories, which emphasise learning through active engagement and real-world contexts, this study examines how specific VR design features, namely, feedback mechanisms (feedback awareness and feedback usefulness) and presence types (social and spatial presence), relate to student motivation and performance in presentations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In a one-group pre-test-post-test field study, 285 university students participated in 30-min individual VR training sessions focused on presentation skills refinement. Using structural equation modelling, this study assessed the relationships between feedback and presence elements with student motivation and performance outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Analysis revealed that feedback awareness and social presence were positively associated with students' motivation to refine their presentation skills. Effective feedback mechanisms, particularly those that enhance feedback awareness and usefulness, were crucial for skill transfer, while increased social presence was associated with improved academic performance in the classroom. Conversely, spatial presence was unexpectedly negatively related to both motivation and performance, suggesting that how VR creates spatial elements may influence learning outcomes. By examining the nuances of these VR features, this study offers valuable insights for software developers and educators aiming to improve university students' presentation skills through VR training applications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144869340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous research has shown that students often struggle to understand algebraic expressions and solve equations, and that traditional instructional approaches may not be sufficient to address these challenges. While the use of technology and physical manipulatives independently has been shown to support students' conceptual understanding in algebra, limited research has examined their combined use—particularly the integration of dynamic mathematics software and manipulatives—within the context of algebra instruction.
Objectives
This study aims to explore seventh-grade students' perceptions of an enriched algebra instruction (EAI) approach that integrates dynamic mathematics software and physical manipulatives to support and enhance their learning experiences.
Methods
A qualitative case study was conducted with 30 Turkish seventh-grade students (aged 12–13). The EAI approach integrated technological tools, concrete materials, collaborative group work, and classroom discussions. Data were collected through a survey comprising 22 items rated on a 5-point Likert-type scale and four open-ended questions, as well as through classroom observations. Quantitative data were analysed using descriptive statistics, while qualitative data were subjected to thematic content analysis.
Results and Conclusion
Most students reported that the use of EAI, which integrates dynamic software and manipulatives, enhanced their active engagement in classroom activities, enabled them to conduct multiple experiments and observations, and facilitated visual interaction with the content. They also indicated that EAI helped them explore relationships within algebraic expressions, which they perceived as contributing to the development of their algebraic thinking and conceptual understanding.
{"title":"Seventh-Grade Students' Views Regarding Enriched Algebra Instruction (EAI) Supported by Technology and Manipulatives","authors":"Osman Birgin, Kayhan Demirören","doi":"10.1111/jcal.70100","DOIUrl":"https://doi.org/10.1111/jcal.70100","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Previous research has shown that students often struggle to understand algebraic expressions and solve equations, and that traditional instructional approaches may not be sufficient to address these challenges. While the use of technology and physical manipulatives independently has been shown to support students' conceptual understanding in algebra, limited research has examined their combined use—particularly the integration of dynamic mathematics software and manipulatives—within the context of algebra instruction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study aims to explore seventh-grade students' perceptions of an enriched algebra instruction (EAI) approach that integrates dynamic mathematics software and physical manipulatives to support and enhance their learning experiences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A qualitative case study was conducted with 30 Turkish seventh-grade students (aged 12–13). The EAI approach integrated technological tools, concrete materials, collaborative group work, and classroom discussions. Data were collected through a survey comprising 22 items rated on a 5-point Likert-type scale and four open-ended questions, as well as through classroom observations. Quantitative data were analysed using descriptive statistics, while qualitative data were subjected to thematic content analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusion</h3>\u0000 \u0000 <p>Most students reported that the use of EAI, which integrates dynamic software and manipulatives, enhanced their active engagement in classroom activities, enabled them to conduct multiple experiments and observations, and facilitated visual interaction with the content. They also indicated that EAI helped them explore relationships within algebraic expressions, which they perceived as contributing to the development of their algebraic thinking and conceptual understanding.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843615","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}
Empirical studies have revealed students' development of computational thinking (CT) and mathematical thinking (MT) during programming-based mathematical problem-solving, highlighting specific CT concepts or practices that serve as learning goals or outcomes. However, implementing programming-based mathematics instruction requires teachers to have sufficient knowledge about learners' thinking processes in such a context, while very little is known about multifaceted solution development from a learner-centred perspective.
Objectives
Viewing CT and MT as processes that go beyond specific skills or concepts, we conducted a qualitative study to investigate how participants develop computational solutions to mathematical problems and construct meaningful understandings of these solutions.
Methods
We adopted an interpretive approach to participants' solution pathways to reveal their diverse thinking processes underlying solution development. A constant comparative analysis approach was undertaken to guide the data analysis.
Results and Conclusions
We identified multiple solution pathways in developing programming-based mathematical solutions (PMS) and characterised four significant pathways comprising seven distinct sub-situations: (1) transition between personal MT and invalid PMS, (2) evolution from invalid PMS to valid PMS, (3) construction from non-meaningful PMS to meaningful PMS and (4) revision from suboptimal PMS to optimal PMS. The findings contribute to a deeper understanding of problem solvers' learning in programming-based mathematical problem-solving and offer implications for theory and practice in programming-rich mathematics education.
{"title":"A Learner-Centred Exploration of Teachers' Solution Pathways in K-12 Programming-Based Mathematical Problem-Solving","authors":"Huiyan Ye, Biyao Liang, Oi-Lam Ng","doi":"10.1111/jcal.70102","DOIUrl":"https://doi.org/10.1111/jcal.70102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Empirical studies have revealed students' development of computational thinking (CT) and mathematical thinking (MT) during programming-based mathematical problem-solving, highlighting specific CT concepts or practices that serve as learning goals or outcomes. However, implementing programming-based mathematics instruction requires teachers to have sufficient knowledge about learners' thinking processes in such a context, while very little is known about multifaceted solution development from a learner-centred perspective.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Viewing CT and MT as processes that go beyond specific skills or concepts, we conducted a qualitative study to investigate how participants develop computational solutions to mathematical problems and construct meaningful understandings of these solutions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We adopted an interpretive approach to participants' solution pathways to reveal their diverse thinking processes underlying solution development. A constant comparative analysis approach was undertaken to guide the data analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>We identified multiple solution pathways in developing programming-based mathematical solutions (PMS) and characterised four significant pathways comprising seven distinct sub-situations: (1) transition between personal MT and invalid PMS, (2) evolution from invalid PMS to valid PMS, (3) construction from non-meaningful PMS to meaningful PMS and (4) revision from suboptimal PMS to optimal PMS. The findings contribute to a deeper understanding of problem solvers' learning in programming-based mathematical problem-solving and offer implications for theory and practice in programming-rich mathematics education.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Weidlich, D. Gašević, H. Drachsler, P. Kirschner
Background
As researchers rush to investigate the potential of AI tools like ChatGPT to enhance learning, well-documented pitfalls threaten the validity of this emerging research. Issues of media comparison research, where the confounding of instructional methods and technological affordances is unrecognised, may render effects uninterpretable.
Objectives
Using a recent meta-analysis by Deng et al. (Computers & Education, 227, 105224) as an example, we revisit key insights from the media/methods debate to highlight recurring conceptual challenges in ChatGPT efficacy studies.
Methods
This conceptual article contrasts nascent ChatGPT research with the more established literature on Intelligent Tutoring Systems to identify three non-negotiable considerations for interpretable effects: (1) descriptions of the precise nature of the experimental treatment and (2) the activities of the control group, as well as (3) outcome measures as valid indicators of learning. To provide some initial evidence, we audited a subset of primary experiments included in Deng et al.'s meta-analysis, demonstrating that only a small minority of studies satisfied all three non-negotiable considerations.
Results and Conclusions
Loosely defined treatments, mismatched or opaque controls, and outcome measures with unclear links to durable learning obscure causal claims of this emerging literature. Observed gains cannot, at this time, be confidently attributed to ChatGPT, and meta-analytics effect sizes may over- or understate its benefits. Progress, we argue, will require rigorous designs, transparent reporting, and a critical stance toward “fast science.”
{"title":"ChatGPT in Education: An Effect in Search of a Cause","authors":"J. Weidlich, D. Gašević, H. Drachsler, P. Kirschner","doi":"10.1111/jcal.70105","DOIUrl":"https://doi.org/10.1111/jcal.70105","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As researchers rush to investigate the potential of AI tools like ChatGPT to enhance learning, well-documented pitfalls threaten the validity of this emerging research. Issues of media comparison research, where the confounding of instructional methods and technological affordances is unrecognised, may render effects uninterpretable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Using a recent meta-analysis by Deng et al. (<i>Computers & Education</i>, 227, 105224) as an example, we revisit key insights from the media/methods debate to highlight recurring conceptual challenges in ChatGPT efficacy studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This conceptual article contrasts nascent ChatGPT research with the more established literature on Intelligent Tutoring Systems to identify three non-negotiable considerations for interpretable effects: (1) descriptions of the precise nature of the experimental treatment and (2) the activities of the control group, as well as (3) outcome measures as valid indicators of learning. To provide some initial evidence, we audited a subset of primary experiments included in Deng et al.'s meta-analysis, demonstrating that only a small minority of studies satisfied all three non-negotiable considerations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Loosely defined treatments, mismatched or opaque controls, and outcome measures with unclear links to durable learning obscure causal claims of this emerging literature. Observed gains cannot, at this time, be confidently attributed to ChatGPT, and meta-analytics effect sizes may over- or understate its benefits. Progress, we argue, will require rigorous designs, transparent reporting, and a critical stance toward “fast science.”</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 5","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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