IEEE Transactions on Learning Technologies最新文献

{"title":"From Discovery to Design and Implementation: A Guide on Integrating Immersive Technologies in Public Safety Training","authors":"Ronak R. Mohanty;Peter Selly;Lindsey Brenner;Shantanu Vyas;Cassidy R. Nelson;Jason B. Moats;Joseph L. Gabbard;Ranjana K. Mehta","doi":"10.1109/TLT.2025.3555649","DOIUrl":"https://doi.org/10.1109/TLT.2025.3555649","url":null,"abstract":"Immersive extended reality (XR) technologies, including augmented reality (AR), virtual reality, and mixed reality, are transforming the landscape of education and training through experiences that promote skill acquisition and enhance memory retention. These technologies have notably improved decision making and situational awareness in public safety training. Despite the promise of these advancements, XR adoption for emergency response has been slow. This hesitancy can be partially attributed to a lack of guidance for integrating these novel technologies into existing curricula. This work aims to guide instructional designers, curriculum developers, and technologists in seamlessly integrating immersive technologies into public safety training curricula. This work provides a comprehensive account of our collaboration with instructional designers, public safety personnel, and subject matter experts in developing an AR-based training curriculum for the Sort, Assess, Life-saving Interventions, Treatment/Transport triage technique used in mass casualty incidents (MCIs). In addition, we introduce a systematic framework for public safety curriculum development based on the Analyze, Design, Develop, Implement, Evaluate instructional design model. Leveraging a human-centered design approach, we first analyze the necessity for immersive learning in public safety. Next, we identify the obstacles in developing XR training experiences and outline our construct of a training prototype through iterative evaluations based on stakeholder feedback. Finally, we share qualitative insights through iterative evaluations with firefighters and emergency medical technicians performing MCI triage tasks in AR, supplemented by survey questionnaires and semistructured interviews. Our goal is to provide a blueprint for a successful integration of immersive technologies into public safety training curricula.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"387-401"},"PeriodicalIF":2.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EduPlanner: LLM-Based Multiagent Systems for Customized and Intelligent Instructional Design","authors":"Xueqiao Zhang;Chao Zhang;Jianwen Sun;Jun Xiao;Yi Yang;Yawei Luo","doi":"10.1109/TLT.2025.3561332","DOIUrl":"https://doi.org/10.1109/TLT.2025.3561332","url":null,"abstract":"Large language models (LLMs) have significantly advanced smart education in the artificial general intelligence era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: 1) <italic>customized generation:</i> generating niche-targeted teaching content based on students' varying learning abilities and states and 2) <italic>intelligent optimization:</i> iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multiagent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students' knowledge levels and learning abilities. In addition, we introduce the CIDDP, an LLM-based 5-D evaluation module encompassing <bold>C</b>larity, <bold>I</b>ntegrity, <bold>D</b>epth, <bold>P</b>racticality, and <bold>P</b>ertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"416-427"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Digital Teacher Appearance Anthropomorphism Impacts Digital Learning Satisfaction and Intention to Use: Interaction With Knowledge Type","authors":"Biao Gao;Jun Yan;Ronghui Zhong","doi":"10.1109/TLT.2025.3560032","DOIUrl":"https://doi.org/10.1109/TLT.2025.3560032","url":null,"abstract":"Digital teachers represent an innovative fusion of media and artificial intelligence (AI) within online educational environments. However, the specific ways in which the appearance anthropomorphism of digital teachers influences the delivery of different knowledge types remain insufficiently understood. Drawing on Embodied Learning Theory and Parasocial Interaction Theory, this study investigates how digital teachers' appearance (cartoonish versus realistic) interacts with knowledge types (explicit versus tacit) to affect digital learning satisfaction and usage intention, exploring the mediating roles of physical and social presence. Initially, we implemented a 2 × 2 experimental design using a large language model application, collecting data from 475 participants to empirically test our hypotheses. Subsequently, in-depth interviews were conducted with 21 Chinese university students to further validate and clarify the underlying mechanisms behind these interactions. The results indicate that digital teachers with a cartoonish appearance are more effective for delivering explicit knowledge, whereas digital teachers with a realistic appearance excel in conveying tacit knowledge. Both physical presence and social presence were found to significantly mediate these effects. This research enriches our understanding of AI-enhanced online education by highlighting the alignment effect between digital teacher appearance and the type of knowledge delivered and by uncovering the underlying psychological mechanisms. In addition, it offers practical insights for the design of digital human appearances in educational interfaces and broader AI–human interaction scenarios.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"438-457"},"PeriodicalIF":2.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course","authors":"Jingjing Chen;Rao Muhammad Aqib Hassan;Shuai Sun;Yilin Mo;Dan Zhang","doi":"10.1109/TLT.2025.3556527","DOIUrl":"https://doi.org/10.1109/TLT.2025.3556527","url":null,"abstract":"The lightboard, an affordable and readily accessible tool, has become a promising approach for enhancing engagement in instructional videos. Despite its potential, previous studies have primarily highlighted the benefits of lightboard videos by evaluating learners' subjective experiences, with limited empirical research examining their impact on learning outcomes. Moreover, the psychological factors underlying the potential advantages of lightboard videos have remained largely unexplored. To address these gaps, the present study conducted an online learning task in a mathematical optimization course, randomly assigning 78 college students to three groups: lightboard, whiteboard, and no-instructor. Learning outcomes and experiences during the learning process were measured and analyzed. The results showed that the lightboard group experienced significantly lower cognitive load while achieving learning outcomes comparable to the other two groups, suggesting that lightboard videos can reduce students' cognitive load without compromising learning outcomes. Further analysis of the psychological factors revealed that cognitive load played a more critical role than perceived social presence or learning motivation in explaining learning outcomes. These findings underscore the positive impact of lightboard videos on online learning, provide insights into the underlying psychological mechanisms, and offer implications for their integration into educational practices.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"428-437"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human–Machine Cocreation: The Effects of ChatGPT on Students’ Learning Performance, AI Awareness, Critical Thinking, and Cognitive Load in a STEM Course Toward Entrepreneurship","authors":"Yu Ji;Zehui Zhan;Tingting Li;Xuanxuan Zou;Siyuan Lyu","doi":"10.1109/TLT.2025.3554584","DOIUrl":"https://doi.org/10.1109/TLT.2025.3554584","url":null,"abstract":"The advent of generative artificial intelligence (GAI), exemplified by ChatGPT, has introduced both new opportunities and challenges in science, technology, engineering, and mathematics (STEM) and entrepreneurship education. This exploratory quasi-experimental study examined the effects of ChatGPT-assisted collaborative learning (CCL) on students’ learning performance, artificial intelligence (AI) awareness, critical thinking, and cognitive load. A total of 36 sophomore undergraduates participated in an eight-week instructional experiment, dedicating 3 h per week to applying STEM and entrepreneurship knowledge in the creation of cultural products. The experimental group (<italic>N</i> = 21) participated in CCL, while the control group (<italic>N</i> = 15) engaged in non-ChatGPT-assisted collaborative learning (NCCL). The results indicated that the CCL group outperformed the NCCL group in terms of learning performance, AI awareness, and cognitive load, while the NCCL group excelled in critical thinking. The findings confirm that ChatGPT offers significant potential and advantages in addressing complex problems within group collaboration and stimulating group creativity, providing new insights into fostering students’ entrepreneurial spirit and skills. However, overreliance on and misuse of ChatGPT may hinder student learning outcomes. Future research should focus on the cocreative problem-solving mechanisms between humans and machines in entrepreneurial education, particularly the interplay of knowledge, thinking, emotions, and actions in collaborative processes involving GAI.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"402-415"},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Academic Performance Prediction Using Machine Learning Approaches: A Survey","authors":"Jialun Pan;Zhanzhan Zhao;Dongkun Han","doi":"10.1109/TLT.2025.3554174","DOIUrl":"https://doi.org/10.1109/TLT.2025.3554174","url":null,"abstract":"Properly predicting students'academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to predict students' academic performance has proven to be less accurate and efficient than desired. Consequently, the past decade has witnessed a marked surge in employing machine learning and data mining techniques to forecast students' performance. However, the academic community has yet to agree on the most effective algorithm for predicting academic outcomes. Nonetheless, conducting an analysis and comparison of the existing algorithms in this field remains meaningful. Furthermore, recommendations for selecting an appropriate algorithm will be provided to interested researchers and educators based on their specific requirements. This article reviews the state-of-the-art literature on academic performance predictions using machine learning approaches in recent years. It details the variables analyzed, the algorithms implemented, the datasets utilized, and the evaluation metrics applied to assess model efficacy. What makes this work different is that relevant surveys in the past 10 years are also analyzed and compared, highlighting their contributions and review methods. In addition, we compared the accuracy of various machine learning models using popular open-access datasets and determined the best-performing algorithms among them. Our dataset and source codes are released for future algorithm comparisons and evaluations in this community.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"351-368"},"PeriodicalIF":2.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motivating Students With Different Needs to Learn Chinese in a Mixed-Background Classroom by Robot-Assisted Learning","authors":"Ka-Yan Fung;Kwong-Chiu Fung;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song","doi":"10.1109/TLT.2025.3551256","DOIUrl":"https://doi.org/10.1109/TLT.2025.3551256","url":null,"abstract":"Mastering the visually complex characters of the Chinese language poses significant challenges for students. The situation is even worse in Hong Kong, where students with different backgrounds, including students with/without dyslexia and non-Chinese speaking (NCS) students, are placed in the same class. Interactive design has been proven effective in enhancing students' learning performance and engagement. However, developing a learning tool for students with diverse backgrounds is challenging. This study proposes a robot-assisted Chinese learning system (<italic>RACLS</i>) for those with diverse backgrounds and investigates its impact on learning motivation by a comparison study. In particular, 39 students participate in a five-day robot-led training program, while another 39 students received traditional teacher-led training. The comparison results show that <italic>RACLS</i> can enhance the emotional engagement of students with dyslexia and strengthen the behavioral engagement of students without dyslexia. Interestingly, the learning motivation of NCS students in the experimental and control groups is enhanced similarly.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"369-386"},"PeriodicalIF":2.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of the Metaverse on Promoting Students’ Access to Quality Education: A Meta-Analysis","authors":"Yuanbin Diao;Yu-Sheng Su","doi":"10.1109/TLT.2025.3550714","DOIUrl":"https://doi.org/10.1109/TLT.2025.3550714","url":null,"abstract":"With technological advancements, the Metaverse is being used to enhance learning effects and learning experience to ensure quality education. However, current empirical studies have produced varying results. Therefore, a meta-analysis was executed, leveraging the capabilities of Version 3 of the Comprehensive Meta-Analysis software to effectively synthesize the data, drawing insights from 34 studies published prior to October 2024. The goal was to analyze the effects of the Metaverse on quality education, and to investigate the moderating influences of four variables: Metaverse tools, educational stages, subject area, and treatment duration. The results showed that the overall effect sizes for learning effects and learning experience were 0.922 and 1.153, respectively, suggesting that the Metaverse substantially influences educational effects and learning experience. The four moderating variables all play a significant role in shaping the influence of the Metaverse on both learning effects and experience. This meta-analysis highlights a striking trend: the Metaverse's effects were especially pronounced for elementary and secondary school students, but less so for university students. In addition, the Metaverse's effects were most significant in science disciplines.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"321-334"},"PeriodicalIF":2.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing English Major Students’ Writing Errors With an Automated Writing Evaluation System: Evidence From Eye-Tracking Technology","authors":"Bei Cai;Ziyu He;Hong Fu;Yang Zheng;Yanjie Song","doi":"10.1109/TLT.2025.3547321","DOIUrl":"https://doi.org/10.1109/TLT.2025.3547321","url":null,"abstract":"Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the AWE system's effectiveness in reducing the writing errors of English major students. A total of 118 higher vocational college students majoring in English in China participated in this eight-week study. The experimental group studied with and received feedback from both the AWE system (Pigai) and the teacher, whereas the control group studied without the AWE system and only received teacher feedback. Eye-tracking experiments were conducted before and after the writing instruction. Participants’ responses during the eye-tracking experiment, first-person eye movement video data, and corresponding gaze data were collected. Leveraging the application of neural network technology in optical character recognition (OCR), combined with data from an eye-tracking device, we developed a system that can transform first-person eye movement video data and gaze data into heatmaps and eye-tracking indices conducive to analysis. Various data analysis methods were employed, including neural network algorithms, heatmap analysis, Mann–Whitney U test, independent-samples <italic>t</i>-test, and Welch's <italic>t</i>-test. The results for the post-eye-tracking experiment responses, heatmaps, and eye-tracking indices indicate the advantages of using the AWE system, which effectively enhances students’ ability to recognize writing errors while reducing processing time by facilitating the internalization of writing errors through continuous feedback on such errors, and enabling them to apply this knowledge to new materials, thereby recognizing writing errors more quickly and accurately, and thus helping them to reduce writing errors. The pedagogical implications are fully discussed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"304-320"},"PeriodicalIF":2.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PythonPal: Enhancing Online Programming Education Through Chatbot-Driven Personalized Feedback","authors":"Sirinda Palahan","doi":"10.1109/TLT.2025.3545084","DOIUrl":"https://doi.org/10.1109/TLT.2025.3545084","url":null,"abstract":"The rise of online programming education has necessitated more effective personalized interactions, a gap that PythonPal aims to fill through its innovative learning system integrated with a chatbot. This research delves into PythonPal's potential to enhance the online learning experience, especially in contexts with high student-to-teacher ratios where there is a need for personalized feedback. PythonPal's design, featuring modules for conversation, tutorials, and exercises, was evaluated through student interactions and feedback. Key findings reveal PythonPal's proficiency in syntax error recognition and user query comprehension, with its intent classification model showing high accuracy. The system's performance in error feedback, though varied, demonstrates both strengths and areas for enhancement. Student feedback indicated satisfactory query understanding and feedback accuracy but also pointed out the need for faster responses and improved interaction quality. PythonPal's deployment promises to significantly enhance online programming education by providing immediate personalized feedback and interactive learning experiences, fostering a deeper understanding of programming concepts among students. These benefits mark a step forward in addressing the challenges of distance learning, making programming education more accessible and effective.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"335-350"},"PeriodicalIF":2.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}