Recommender Systems in E-learning

Abstract

In this era when every aspect of society is accelerating, people are always seeking improvement to stay competitive in their careers. E-learning systems fit into the ever challenging situation and provide learners with remote learning opportunities and abundant learning resources. Facing with the numerous resources online, users need support in deciding which course to take, thus recommender systems are applied in E-learning to provide learners with personalized services by automatically identifying their preferences. This position paper systematically discusses the main recommendation techniques employed in in E-learning and identifies new research directions. Three main recommendation techniques are reviewed in this paper: content-based, collaborative filtering-based and knowledge-based recommendations. The basic mechanism of these technique together with how they are used to fulfill the specific requirements in the context of E-learning are highlighted and presented. The observations in this paper could support researchers and practitioners to better understand the current development and future directions of recommender systems in E-learning.