Recommenders in improving students’ engagement in large scale open learning

Abstract

Technology Enhanced Learning recommender systems (TEL RS) have become an attractive research area in the recent decade as they promote users’ selection process within limited time in educational domain. Actually, they have the ability to support e-learning through personalization of the learning process whether for teachers’ requirements or for individual students’ needs. Conventional recommender systems have proposed various methods focusing on recommendations to individual learners. Recently, due to significant increase in students’ number, especially in the field of massive open online courses (MOOCs) and regarding their diversity, the need of offering adaptation is becoming more important to help improve learning quality. For that reason, several recommender systems have been developed to adapt learning to personal students’ needs in MOOC context. This paper reports and discuss recommendations in large scale open learning for improving students’ engagement. We present an analysis and a comparison between TEL RS in the context of MOOCs for different purposes. Then, we present our proposed recommender system of learning activities in accordance with a set of comparison criteria.