Abstract:
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 learning more effectively and assist large
class online teaching with muiti-background students.
