A Personalized e - Learning Material Recommender System
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چکیده
E-learning environments are mainly based on a range of delivery and interactive services. Web-based personalized learning recommender systems can, as a kind of services in e-learning environment, provide learning recommendations to students. This research proposes a framework of a personalized learning recommender system, which aims to help students find learning materials they would need to read. Two related technologies are developed under the framework: one is a multi-attribute evaluation method to justify a student's need, and another is a fuzzy matching method to find suitable learning materials to best meet each student need. The implementation of this proposed personalized learning recommender system can support students online learn ing more effectively and assist large class online teaching with multi-background students.
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