A Semantic Web Architecture for Context Recommendation System in E-learning Applications

Abstract

The widespread use of e-learning applications has put emphasis on the importance of having applications more personalized and adaptable to every learner needs. The one size fits all is no more working. Every learner should be delivered the right learning material that suits its learning context at the right time. The challenge is to incorporate the recommendation system in e-learning platforms in order to offer to learners a successful learning experience. In response to this challenge, in this paper, we propose a semantic web architecture of a context recommendation system in e-learning by means of which the learners will be offered learning content based on their profiles, activities and social interactions. The proposed architecture is a re-engineering of classical web architecture of current e-learning platforms. It’s based on semantic web technologies. It comprises an ontology that guarantees a shareable and reusable modeling of the learning context and OWL Rules filtering that will be used as a recommendation technique.