SEMANTIC BASED E-LEARNING RECOMMENDER SYSTEM

Introduction of new technologies in the last few decades have brought about some innovative methods in web-based education. However many of these online courses provide universal static solution which do not cater the individual needs of the learner. Recommender system has been successfully recommending items such as books, movies, news articles etc however recommendation techniques applied in the e-learning domain are relatively new. Many of the techniques applied in the e-learning domain are generic and usually derived from other domains. This paper will present semantic based recommender system for e-learner to facilitate effective learning. We use a novel alternative to conventional recommendation techniques by considering a social network tool such as twitter which is popular for information sharing. Relevant tweets are recommended to the learner as per the current learning topic of the learner