Personalize Recommendation Approach for Web Search in E-Learning

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. E-Learning recommendation system helps learners to make choices without sufficient personal experience of the alternatives, and it is considerably requisite in this information explosion age. In our study, the user-based collaborative filtering method is chosen as the primary recommendation algorithm, combined with online education. We analyze the requirement of a web based E-Learning recommendation system, The proposed system is based on four modules, A student Profiling Module takes Students all Personal and Academic Information, Behavioral Activity analyzer module is use to identify learners learning preferences and all activities which is done at the time of web surfing by student and a recommendation module which pre-processes data to create a suitable recommendation list and predicting the student interest domain.