With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.
{"title":"Navigating the Textual Maze: Enhancing Textual Analytical Skills Through an Innovative GAI Prompt Framework","authors":"Xuefan Li;Tingsong Li;Minjuan Wang;Sining Tao;Xiaoxu Zhou;Xiaoqing Wei;Naiqing Guan","doi":"10.1109/TLT.2025.3539104","DOIUrl":"https://doi.org/10.1109/TLT.2025.3539104","url":null,"abstract":"With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"206-215"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521505","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}
Pub Date : 2025-02-05DOI: 10.1109/TLT.2025.3539179
Kaitlyn Tracy;Ourania Spantidi
Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed.
{"title":"Impact of GPT-Driven Teaching Assistants in VR Learning Environments","authors":"Kaitlyn Tracy;Ourania Spantidi","doi":"10.1109/TLT.2025.3539179","DOIUrl":"https://doi.org/10.1109/TLT.2025.3539179","url":null,"abstract":"Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"192-205"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496548","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}
Pub Date : 2025-02-03DOI: 10.1109/TLT.2025.3537802
Ahmad Chaddad;Yuchen Jiang
The concept of the Metaverse, viewed as the ultimate manifestation of the Internet, has gained significant attention due to rapid advances in technologies such as the Internet of Things (IoT) and blockchain. Acting as a bridge between the physical and virtual worlds, the Metaverse has the potential to offer remarkable experiences to its users. This study presents a comprehensive survey of Metaverse techniques, including artificial intelligence, blockchain, IoT, augmented reality, virtual reality, 5G, natural language processing, and digital twins. These Metaverse techniques lead to improved health outcomes and patient care, offering innovative treatments for complex conditions, and advancing medical education. We explore the benefits of the Metaverse by examining its effectiveness in supporting various medical applications and highlight potential research challenges and future trends for the medical Metaverse and education. Although the Metaverse is currently in its early stages, more efforts are required to enable its widespread adoption in the future.
{"title":"Integrating Technologies in the Metaverse for Enhanced Healthcare and Medical Education","authors":"Ahmad Chaddad;Yuchen Jiang","doi":"10.1109/TLT.2025.3537802","DOIUrl":"https://doi.org/10.1109/TLT.2025.3537802","url":null,"abstract":"The concept of the Metaverse, viewed as the ultimate manifestation of the Internet, has gained significant attention due to rapid advances in technologies such as the Internet of Things (IoT) and blockchain. Acting as a bridge between the physical and virtual worlds, the Metaverse has the potential to offer remarkable experiences to its users. This study presents a comprehensive survey of Metaverse techniques, including artificial intelligence, blockchain, IoT, augmented reality, virtual reality, 5G, natural language processing, and digital twins. These Metaverse techniques lead to improved health outcomes and patient care, offering innovative treatments for complex conditions, and advancing medical education. We explore the benefits of the Metaverse by examining its effectiveness in supporting various medical applications and highlight potential research challenges and future trends for the medical Metaverse and education. Although the Metaverse is currently in its early stages, more efforts are required to enable its widespread adoption in the future.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"216-229"},"PeriodicalIF":2.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521489","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}
The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.
{"title":"Microlearning in Immersive Virtual Reality: A User-Centered Analysis of Learning Interfaces","authors":"Amarpreet Gill;Derek Irwin;Linjing Sun;Dave Towey;Gege Zhang;Yanhui Zhang","doi":"10.1109/TLT.2025.3533360","DOIUrl":"https://doi.org/10.1109/TLT.2025.3533360","url":null,"abstract":"The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"165-178"},"PeriodicalIF":2.9,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464340","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}
Pub Date : 2025-01-21DOI: 10.1109/TLT.2025.3532464
Guodong Yang;Yan Yan;Shaoqing Guo;Xiaodong Wei
In early education, reading difficulties can lead to negative outcomes. Augmented reality (AR) storybooks combine the benefits of e-books and print books, significantly aiding children's reading skills and gaining recognition from scholars and educators. However, the existing AR storybooks often overlook the design of interactive features, which may explain the inconsistent findings in research on their impact. This study aims to embed interactive tasks into AR storybooks and investigate their effects on children's reading engagement, story retelling, and reading comprehension. In total, 40 children aged eight to ten years were invited to participate in the reading activity. They were randomly assigned to an experimental group and a control group. The experimental group used AR storybooks that included interactive tasks, requiring them to complete various activities during reading. The control group used AR storybooks without interactive tasks, which provided multisensory experiences. Throughout the activity, researchers observed each child's reading engagement and completed a reading engagement assessment form. At the end of the activity, all children completed story retelling and reading comprehension tests. Finally, both groups of children participated in semistructured interviews for cross validation. The study found that children in the experimental group showed significantly higher levels of reading engagement, story retelling, and reading comprehension than children in the control group. While multimedia elements in AR storybooks can increase children's reading engagement, a large part of that engagement is driven by children's focus on AR elements. However, interactive tasks shift children's engagement more toward the story content. We also discovered that interactive tasks are a key factor in encouraging children to think actively and serve as an effective strategy for guiding them to focus on the main issues in the story. In addition, the strategy search decision feedback within the interactive tasks greatly aids children in understanding and remembering the story.
{"title":"The Impact of Embedding Interactive Tasks in Augmented Reality Storybooks on Children's Reading Engagement and Reading Comprehension","authors":"Guodong Yang;Yan Yan;Shaoqing Guo;Xiaodong Wei","doi":"10.1109/TLT.2025.3532464","DOIUrl":"https://doi.org/10.1109/TLT.2025.3532464","url":null,"abstract":"In early education, reading difficulties can lead to negative outcomes. Augmented reality (AR) storybooks combine the benefits of e-books and print books, significantly aiding children's reading skills and gaining recognition from scholars and educators. However, the existing AR storybooks often overlook the design of interactive features, which may explain the inconsistent findings in research on their impact. This study aims to embed interactive tasks into AR storybooks and investigate their effects on children's reading engagement, story retelling, and reading comprehension. In total, 40 children aged eight to ten years were invited to participate in the reading activity. They were randomly assigned to an experimental group and a control group. The experimental group used AR storybooks that included interactive tasks, requiring them to complete various activities during reading. The control group used AR storybooks without interactive tasks, which provided multisensory experiences. Throughout the activity, researchers observed each child's reading engagement and completed a reading engagement assessment form. At the end of the activity, all children completed story retelling and reading comprehension tests. Finally, both groups of children participated in semistructured interviews for cross validation. The study found that children in the experimental group showed significantly higher levels of reading engagement, story retelling, and reading comprehension than children in the control group. While multimedia elements in AR storybooks can increase children's reading engagement, a large part of that engagement is driven by children's focus on AR elements. However, interactive tasks shift children's engagement more toward the story content. We also discovered that interactive tasks are a key factor in encouraging children to think actively and serve as an effective strategy for guiding them to focus on the main issues in the story. In addition, the strategy search decision feedback within the interactive tasks greatly aids children in understanding and remembering the story.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"179-191"},"PeriodicalIF":2.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496529","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}
Pub Date : 2025-01-15DOI: 10.1109/TLT.2024.3513373
Minjuan Wang;John Chi-Kin Lee
{"title":"Editorial: Journey to the Future: Extended Reality and Intelligence Augmentation","authors":"Minjuan Wang;John Chi-Kin Lee","doi":"10.1109/TLT.2024.3513373","DOIUrl":"https://doi.org/10.1109/TLT.2024.3513373","url":null,"abstract":"","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"53-55"},"PeriodicalIF":2.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992955","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}
Pub Date : 2025-01-15DOI: 10.1109/TLT.2025.3529994
Nicolas Pope;Juho Kahila;Henriikka Vartiainen;Matti Tedre
The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K–12 computing education. In response, we introduce “GenAI Teachable Machine,” a visual, data-driven design platform aimed at introducing novice learners to fundamental ML concepts and workflows, particularly in the context of classifiers. Following the design science research (DSR) method, this study presents the prior recommendations, standards, codevelopment, and extensive field testing that resulted in a platform enabling young learners to express their own interest-driven ideas through codesigning and sharing personally meaningful apps. The platform improves on the design of Google's popular Teachable Machine 2 by its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. It also enables one to distribute the collection of training data among many users. In addition to the DSR process, this article presents findings from usability lab tests (N = 8) and 6-h classroom projects involving fourth and seventh grade children (N = 213). The results show that children who had no experience of ML were able to navigate through the workflow and turn their own ideas into concrete ML-based apps. The majority of children were able to reflect and present, in their own words, their working process using data-driven (design) thinking concepts and insights.
{"title":"Children's AI Design Platform for Making and Deploying ML-Driven Apps: Design, Testing, and Development","authors":"Nicolas Pope;Juho Kahila;Henriikka Vartiainen;Matti Tedre","doi":"10.1109/TLT.2025.3529994","DOIUrl":"https://doi.org/10.1109/TLT.2025.3529994","url":null,"abstract":"The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K–12 computing education. In response, we introduce “GenAI Teachable Machine,” a visual, data-driven design platform aimed at introducing novice learners to fundamental ML concepts and workflows, particularly in the context of classifiers. Following the design science research (DSR) method, this study presents the prior recommendations, standards, codevelopment, and extensive field testing that resulted in a platform enabling young learners to express their own interest-driven ideas through codesigning and sharing personally meaningful apps. The platform improves on the design of Google's popular Teachable Machine 2 by its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. It also enables one to distribute the collection of training data among many users. In addition to the DSR process, this article presents findings from usability lab tests (<italic>N</i> = 8) and 6-h classroom projects involving fourth and seventh grade children (<italic>N</i> = 213). The results show that children who had no experience of ML were able to navigate through the workflow and turn their own ideas into concrete ML-based apps. The majority of children were able to reflect and present, in their own words, their working process using data-driven (design) thinking concepts and insights.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"130-144"},"PeriodicalIF":2.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361071","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}
Pub Date : 2025-01-10DOI: 10.1109/TLT.2025.3526863
Jamie I. Cross;Christine C. Boag-Hodgson
The incorporation of immersive technologies into student pilot training has been hindered by a lack of empirical evidence to support their efficacy. Existing research on virtual reality flight simulators is limited in scope, predominantly focused on single-users in small, piston-engine aircraft, with little concern for its application to commercial pilot operations. This article initiates the process of evaluating a virtual reality flight simulator to train ab-initio pilots in a multicrew environment using a complex jet aircraft (a Boeing 737-800). An experimental design-based research methodology was initially employed to identify and address any methodological issues. To demonstrate proof of concept, the study evaluated two different scenarios and assessed the performance of two head-mounted displays. Additionally, the research included measures of situational awareness and workload. The setup was configured to allow the evaluation of various combinations of virtual reality and desktop flight simulators within a multicrew environment. Valuable insights have been gained in creating a reliable environment for further research on collaborative virtual reality flight simulators. Proof of concept was demonstrated through satisfactory usability and fidelity in a two-pilot virtual reality simulator. The study confirmed that participants can effectively collaborate in a virtual environment during simulator sessions modeled on a typical initial First Officer airline training program for complex commercial aircraft. Participants in the virtual environment exhibited reduced workload (effort) in comparison to a desktop flight simulator, indicating a potential decrease in cognitive processing. This, in turn, suggests enhanced spatial memory, corroborated by measures of heightened team situational awareness in the virtual environment. The benefits of these findings are numerous, including the potential for a virtual reality flight simulator to supplement traditional pilot training methods.
{"title":"A Collaborative Virtual Reality Flight Simulator: Efficacy, Challenges, and Potential","authors":"Jamie I. Cross;Christine C. Boag-Hodgson","doi":"10.1109/TLT.2025.3526863","DOIUrl":"https://doi.org/10.1109/TLT.2025.3526863","url":null,"abstract":"The incorporation of immersive technologies into student pilot training has been hindered by a lack of empirical evidence to support their efficacy. Existing research on virtual reality flight simulators is limited in scope, predominantly focused on single-users in small, piston-engine aircraft, with little concern for its application to commercial pilot operations. This article initiates the process of evaluating a virtual reality flight simulator to train ab-initio pilots in a multicrew environment using a complex jet aircraft (a Boeing 737-800). An experimental design-based research methodology was initially employed to identify and address any methodological issues. To demonstrate proof of concept, the study evaluated two different scenarios and assessed the performance of two head-mounted displays. Additionally, the research included measures of situational awareness and workload. The setup was configured to allow the evaluation of various combinations of virtual reality and desktop flight simulators within a multicrew environment. Valuable insights have been gained in creating a reliable environment for further research on collaborative virtual reality flight simulators. Proof of concept was demonstrated through satisfactory usability and fidelity in a two-pilot virtual reality simulator. The study confirmed that participants can effectively collaborate in a virtual environment during simulator sessions modeled on a typical initial First Officer airline training program for complex commercial aircraft. Participants in the virtual environment exhibited reduced workload (effort) in comparison to a desktop flight simulator, indicating a potential decrease in cognitive processing. This, in turn, suggests enhanced spatial memory, corroborated by measures of heightened team situational awareness in the virtual environment. The benefits of these findings are numerous, including the potential for a virtual reality flight simulator to supplement traditional pilot training methods.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"119-129"},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361070","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}
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%$sim$90% missing observations) in most real-world applications. This data sparsity presents challenges to using learner models to effectively predict learners' future performance and explore new hypotheses about learning. This article proposes a systematic framework for augmenting learning performance data to address data sparsity. First, learning performance data can be represented as a 3-D tensor with dimensions corresponding to learners, questions, and attempts, effectively capturing longitudinal knowledge states during learning. Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing (KT) tasks that predict missing performance values based on real observations. Third, data augmentation using generative artificial intelligence models, including generative adversarial network (GAN), specifically vanilla GANs and generative pretrained transformers (GPTs, specifically GPT-4o), generate data tailored to individual clusters of learning performance. We tested this systemic framework on adult literacy datasets from AutoTutor lessons developed for adult reading comprehension. We found that tensor factorization outperformed baseline KT techniques in tracing and predicting learning performance, demonstrating higher fidelity in data imputation, and the vanilla GAN-based augmentation demonstrated greater overall stability across varying sample sizes, whereas GPT-4o-based augmentation exhibited higher variability, with occasional cases showing closer fidelity to the original data distribution. This framework facilitates the effective augmentation of learning performance data, enabling controlled, cost-effective approach for the evaluation and optimization of ITS instructional designs in both online and offline environments prior to deployment, and supporting advanced educational data mining and learning analytics.
{"title":"Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI","authors":"Liang Zhang;Jionghao Lin;John Sabatini;Conrad Borchers;Daniel Weitekamp;Meng Cao;John Hollander;Xiangen Hu;Arthur C. Graesser","doi":"10.1109/TLT.2025.3526582","DOIUrl":"https://doi.org/10.1109/TLT.2025.3526582","url":null,"abstract":"Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%<inline-formula><tex-math>$sim$</tex-math></inline-formula>90% missing observations) in most real-world applications. This data sparsity presents challenges to using learner models to effectively predict learners' future performance and explore new hypotheses about learning. This article proposes a systematic framework for augmenting learning performance data to address data sparsity. First, learning performance data can be represented as a 3-D tensor with dimensions corresponding to learners, questions, and attempts, effectively capturing longitudinal knowledge states during learning. Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing (KT) tasks that predict missing performance values based on real observations. Third, data augmentation using generative artificial intelligence models, including generative adversarial network (GAN), specifically vanilla GANs and generative pretrained transformers (GPTs, specifically GPT-4o), generate data tailored to individual clusters of learning performance. We tested this systemic framework on adult literacy datasets from AutoTutor lessons developed for adult reading comprehension. We found that tensor factorization outperformed baseline KT techniques in tracing and predicting learning performance, demonstrating higher fidelity in data imputation, and the vanilla GAN-based augmentation demonstrated greater overall stability across varying sample sizes, whereas GPT-4o-based augmentation exhibited higher variability, with occasional cases showing closer fidelity to the original data distribution. This framework facilitates the effective augmentation of learning performance data, enabling controlled, cost-effective approach for the evaluation and optimization of ITS instructional designs in both online and offline environments prior to deployment, and supporting advanced educational data mining and learning analytics.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"145-164"},"PeriodicalIF":2.9,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430503","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}
Pub Date : 2025-01-06DOI: 10.1109/TLT.2025.3525949
Malte Rolf Teichmann
Due to the rise of virtual reality and the—at least now—hypothetical construct of the Metaverse, learning processes are increasingly transferred to immersive virtual learning environments. While the literature provides few design guidelines, most papers miss an application and evaluation description of the design and development processes. As a result, few standardized design processes and related design frameworks exist that meaningfully integrate existing stand-alone design theories and resulting approaches for developing immersive virtual learning environments. The article tackles this challenge with a research procedure based on the design science research method to outline and communicate a Design process framework to create virtual learning environments based on real-world processes for the Edu-Metaverse. The simply applicable artifact represents a comprehensive five-step solution to a well-defined problem by combining interdisciplinary perspectives. It contributes to the concretization of the hypothetical term Metaverse in its intended domain. As a result, practitioners and researchers with different experience levels can use the low-threshold framework.
{"title":"How to Design Immersive Virtual Learning Environments Based on Real-World Processes for the Edu-Metaverse—A Design Process Framework","authors":"Malte Rolf Teichmann","doi":"10.1109/TLT.2025.3525949","DOIUrl":"https://doi.org/10.1109/TLT.2025.3525949","url":null,"abstract":"Due to the rise of virtual reality and the—at least now—hypothetical construct of the <italic>Metaverse</i>, learning processes are increasingly transferred to <italic>immersive virtual learning environments</i>. While the literature provides few design guidelines, most papers miss an application and evaluation description of the design and development processes. As a result, few standardized design processes and related design frameworks exist that meaningfully integrate existing stand-alone design theories and resulting approaches for developing <italic>immersive virtual learning environments</i>. The article tackles this challenge with a research procedure based on the design science research method to outline and communicate a <italic>Design process framework to create virtual learning environments based on real-world processes for the Edu-Metaverse</i>. The simply applicable artifact represents a comprehensive five-step solution to a well-defined problem by combining interdisciplinary perspectives. It contributes to the concretization of the hypothetical term <italic>Metaverse</i> in its intended domain. As a result, practitioners and researchers with different experience levels can use the low-threshold framework.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"100-118"},"PeriodicalIF":2.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106036","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：