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Direct linkHao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
Download full textDanial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
Stone, Melissa L.; Kent, Kevin M.; Roscoe, Rod D.; Corley, Kathleen M.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2017
This chapter explores three broad principles of user-centered design methodologies: participatory design, iteration, and usability considerations. The authors highlight the importance of considering teachers as a prominent type of ITS end user, by describing the barriers teachers face as users and their role in educational technology design. To…
Descriptors: Intelligent Tutoring Systems, Design, Usability, Barriers
Bell, Herbert H.; Reigeluth, Charles M. – Educational Technology, 2014
All branches of the U.S. military are moving toward education and training that is tailored to meet individuals' needs wherever they are and whenever they are ready to learn. The result will be a culture of continuous, personalized learning with learner progress that is based on proficiency and a greater emphasis on technology-enabled immersive…
Descriptors: Military Training, Military Service, Educational Change, Individualized Instruction
Lim, Wei-Ying; So, Hyo-Jeong; Tan, Seng-Chee – Interactive Learning Environments, 2010
While the growing prevalence of Web 2.0 in education opens up exciting opportunities for universities to explore expansive, new literacies practices, concomitantly, it presents unique challenges. Many universities are changing from a content delivery paradigm of eLearning 1.0 to a learner-focused paradigm of eLearning 2.0. In this article, we…
Descriptors: Models, Internet, Electronic Learning, Technology Uses in Education
Retalis, Symeon; Papasalouros, Andreas – Educational Technology & Society, 2005
Educational Adaptive Hypermedia Applications (EAHA) provide personalized views on the learning content to individual learners. They also offer adaptive sequencing (navigation) over the learning content based on rules that stem from the user model requirements and the instructional strategies. EAHA are gaining the focus of the research community as…
Descriptors: Barriers, Models, Educational Strategies, Engineering
Sampson, Demetrios G., Ed.; Spector, J. Michael, Ed.; Ifenthaler, Dirk, Ed.; Isaias, Pedro, Ed. – International Association for Development of the Information Society, 2013
These proceedings contain the papers of the IADIS International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2013), October 22-24, 2013, which has been organized by the International Association for Development of the Information Society (IADIS), co-organized by The University of North Texas (UNT), sponsored by the…
Descriptors: Conference Papers, Cognitive Processes, Learning Processes, Short Term Memory
