E-LEARNING PERSONALIZATION BASED ON COLLABORATIVE FILTERING AND LEARNER’S PREFERENCE

Personalized e-learning based on recommender system is recognized as one of the most interesting research field in the education and teaching in this last decade, since, the learning style is specific for each student. In fact from the knowledge of his or her learning style; it is easier to recommend a teaching strategy builds around a collection of the most adequate learning objects to give a better return on the educational level. This work focuses on the design of a personalized e-learning environment based on collaborative filtering and learning styles. Using the learner profile, the device proposed a personalized teaching strategy by selecting and sequencing learning objects fitting with the learners’ learning styles. Moreover, an experiment was conducted to evaluate the performance of our approach. The result reveals the system effectiveness for which it appears that the proposed approach may be promising.