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Hani Y. Ayyoub; Omar S. Al-Kadi – IEEE Transactions on Learning Technologies, 2024
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that…
Descriptors: Cognitive Style, Individualized Instruction, Learning Management Systems, Artificial Intelligence
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Lemantara, Julianto; Hariadi, Bambang; Sunarto, M. J. Dewiyani; Amelia, Tan; Sagirani, Tri – IEEE Transactions on Learning Technologies, 2023
A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a learning management system (LMS). This article aims to…
Descriptors: Students, Cheating, Prediction, Essays
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Okubo, Fumiya; Shiino, Tetsuya; Minematsu, Tsubasa; Taniguchi, Yuta; Shimada, Atsushi – IEEE Transactions on Learning Technologies, 2023
In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are…
Descriptors: Learning Management Systems, Student Evaluation, Automation, Artificial Intelligence
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Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence
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Sergio Tirado-Olivares; Rocio Minguez-Pardo; Javier del Olmo-Munoz; Jose A. Gonzalez-Calero – IEEE Transactions on Learning Technologies, 2025
Decimal misconceptions are a persistent challenge in mathematics education, often hindering students' long-term understanding. This study examines how learning analytics (LA) can be effectively integrated into instructional sequences to address these misconceptions, providing teachers with real-time insights for formative assessment. Despite the…
Descriptors: Learning Analytics, Elementary School Students, Elementary School Mathematics, Mathematics Education
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Shard; Kumar, Devesh; Koul, Sapna; Siringoringo, Hotniar – IEEE Transactions on Learning Technologies, 2023
Students' and instructors' adoption of "e-learning management systems (e-LMSs)" is critical to their success in a "virtual learning environment." Students can use "e-learning" to obtain instructional materials to supplement "traditional classroom" instruction. This study intends to highlight the important…
Descriptors: Foreign Countries, Students, Behavior, Intention
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Cleon Xavier; Luiz Rodrigues; Newarney Costa; Rodrigues Neto; Gabriel Alves; Taciana Pontual Falcao; Dragan Gasevic; Rafael Ferreira Mello – IEEE Transactions on Learning Technologies, 2025
Providing timely and personalized feedback on open-ended student responses is a challenge in education due to the increased workloads and time constraints educators face. While existing research has explored how learning analytic approaches can support feedback provision, previous studies have not sufficiently investigated educators' perspectives…
Descriptors: Teacher Empowerment, Learning Analytics, Artificial Intelligence, Computer Software
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Liu, Fang; Zhao, Liang; Zhao, Jiayi; Dai, Qin; Fan, Chunlong; Shen, Jun – IEEE Transactions on Learning Technologies, 2022
Educational process mining is now a promising method to provide decision-support information for the teaching-learning process via finding useful educational guidance from the event logs recorded in the learning management system. Existing studies mainly focus on mining students' problem-solving skills or behavior patterns and intervening in…
Descriptors: Data Use, Learning Management Systems, Problem Solving, Learning Processes
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Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
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Roshan Lal Neupane; Prasad Calyam; Songjie Wang; Kiran Neupane; Ashish Pandey; Xiyao Cheng; Durbek Gafurov; Hemanth Sai Yeddulapalli; Noah Glaser; Kanu Priya Singh; Yuanyuan Gu; Shangman Li; Sharan Srinivas – IEEE Transactions on Learning Technologies, 2024
Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the…
Descriptors: Online Courses, Computer Assisted Instruction, Learning Management Systems, Computer Assisted Design
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Andriamiseza, Rialy; Silvestre, Franck; Parmentier, Jean-Francois; Broisin, Julien – IEEE Transactions on Learning Technologies, 2023
Formative assessment provides teachers with feedback to help them adapt their behavior. To manage the increasing number of students in higher education, technology-enhanced formative assessment tools can be used to maintain and hopefully improve teaching and learning quality, thanks to the high amount of data that are generated by their usage.…
Descriptors: Learning Analytics, Formative Evaluation, Evidence Based Practice, Peer Evaluation
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Lin, Jian-Wei; Koong Lin, Hao-Chiang; Chen, Hong-Ren – IEEE Transactions on Learning Technologies, 2022
Conventional e-learning platforms require a high self-regulatory learning (SRL) ability to ensure learning effectiveness. However, because not everyone has high autonomy and a high SRL ability, many students quit during the online learning period. To enhance the SRL ability, many studies have developed e-learning platforms based on Zimmerman's SRL…
Descriptors: Metacognition, Role Models, Learning Strategies, Personal Autonomy
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Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
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Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior