Context-Aware Recommendation of Learning Resources Using Rules Engine

Although numerous studies have been conducted on learning resources recommendation in E-Learning, research extending this investigation into usage of users' contextual information is rare. This paper presents an innovative architecture of an intelligent personalized context-aware recommendation system in an E-Learning environment. The system offers users by recommending learning materials, tutors, or other learners with common interests combining users' social tags from external social network and tags about learning materials from autologous collaborative tagging system. A Rules Engine is used to manage a set of rules for each user to achieve personalized recommendation. The rules which define the relationship between tags construct the relationship graph model for users and learning resources thus graph-based collaborative filtering recommendation methods can be implemented. The rules will be adjusted depending on the user's feedbacks of previous recommendations.