No abstract

No abstract

International audienc

Recently, personalization in Technology Enhanced Learning (TEL) has been researched extensively. With the spreading of online learning environments (OLE) as MOOCs and LMSs, a large number of learners with different characteristics and backgrounds can follow online courses. To support personalization, recommender systems can be used to provide each learner with learning objects helping him to reach his learning objectives. These recommendations are more specific to compute than usual recommendations (like consumer products). Furthermore, if they are included in a course, they depend not only on the learner's profile but also on the content of the course, because they need to fit with the course format at any point. At the same time, there is a growing number of open educational resources (OER) available to usefully enrich the content of online courses. To facilitate their reuse some of these OERs are described with metadata schemas following Linked Open Data principles (LOD). In this paper, we introduce a MOOC-based OER recommender system (MORS) that can be plugged in an OLE to provide recommendations of OERs to learners based on their profiles, the course profile and a process for calculating recommendations based on OERs metadata. This paper presents our approach that has been implemented in a MOOC platform: Open edX. However the proposed approach could be implemented in any OLE by using the same process to calculate recommendations, as long as the learner and the course profiles can be extracted.