A Framework of Semantic Recommender System for e-Learning

Abstract

With the rapid increasing of learning objects (LOs) in a variety of media formats, it becomes quite difficult and complicated task for learners to find suitable LOs based on their needs and preferences. To support personalization, recommender systems can be used to assist learners in finding the appropriate LOs which will be needed for their learning. In this paper, we propose a framework of a semantic recommender system for e-learning in which it will assist learners to find and select the relevant LOs to their field of interest. The proposed framework utilizes the intra and extra semantic relationships between LOs and the learner's needs to provide personalized recommendations for learners. The semantic recommendation algorithm is based on the extension of the query keywords by using the semantic relations, concepts and reasoning means in the domain ontology. The proposed system can be used to reduce the time and effort involved in finding suitable LOs, and thus, improves the quality of learning.