Personalized recommender system for e-Learning environment based on student's preferences

Nowadays, new technologies and the fast increase of the Internet have made access to information easier for all kind of people, building new challenges for education when utilizing the Internet as a tool. One of the best examples is how to personalize an e-learning system according to the learner's requirements and knowledge level in a learning process. This system should adapt the learning experience according to the goals of the individual learner. In this paper, we present a recommender e-learning approach which utilizes recommendation techniques for educational data mining specifically for identifying e-Learners' learning preferences. The proposed approach is based on three modules, a domain module which contains all the knowledge for a particular area, a learner module which uses to identify learners' learning preferences and activities and a recommendation module which pre-processes data to create a suitable recommendation list and predicting performances. Recommended resources are obtained by using level of knowledge of learners in different steps and the range of recommendation techniques based on content-based filtering and collaborative approaches. Several techniques such as classification, clustering and association rules are used to improve personalization with filtering techniques to provide a recommendation and assist learners to improve their performance.