Personalized recommender system for e-Learning environment

Traditional e-Learning environments are based on static contents considering that all learners are similar, so they are not able to respond to each learner’s needs. These systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student’s interactions and preferences. New educational systems should appear to ensure the personalization of learning contents. This work aims to develop a new personalization approach that provides to students the best learning materials according to their preferences, interests, background knowledge, and their memory capacity to store information. A new recommendation approach based on collaborative and content-based filtering is presented: NPR_eL (New multi-Personalized Recommender for e Learning). This approach was integrated in a learning environment in order to deliver personalized learning material. We demonstrate the effectiveness of our approach through the design, implementation, analysis and evaluation of a personal learning environment.