Personalized e-learning system using Item Response Theory
Computers & Education
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
The Use of IRT for Adaptive Item Selection in Item-Based Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Item difficulty estimation: An auspicious collaboration between data and judgment
Computers & Education
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Personalized service is becoming increasingly important, especially in E-learning field. Most personalized E-learning systems only take learners preferences, interests and browsing behaviors into consideration. These systems usually neglect considering whether the learners ability and the difficulty level of recommended learning materials are matched to each other or not. This paper proposes a personalized E-learning system using fuzzy set based clustering algorithm which considers both course materials' difficulty and learners' ability to provide appropriate learning stuffs for learners individually, to help learners learn more efficiently and effectively.