Profiling students' adaptation styles in Web-based learning
Computers & Education
Intelligent Student Profiling with Fuzzy Models
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 3 - Volume 3
Learning style, learning patterns, and learning performance in a WebCT-based MIS course
Information and Management
Authoring of learning styles in adaptive hypermedia: problems and solutions
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
The impact of learning styles on student grouping for collaborative learning: a case study
User Modeling and User-Adapted Interaction
SMAP '06 Proceedings of the First International Workshop on Semantic Media Adaptation and Personalization
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
IEEE Transactions on Neural Networks
Using Clustering Technique for Students' Grouping in Intelligent E-Learning Systems
USAB '08 Proceedings of the 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for Education and Work
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In the paper, the data driven approach for users' modeling in intelligent e-learning system is considered. Individual models are based on preferred learning styles dimensions, according to which students focus on different types of information and show different performances in educational process. Building individual models of learners allows for adjusting teaching paths and materials into their needs. In the presented approach, students are divided into groups by unsupervised classification. Application of two-phase hierarchical clustering algorithm which enables tutors to determine such parameters as maximal number of groups, clustering threshold and weights for different learning style dimensions is described. Experimental results connected with modeling real groups of students are discussed.