MORS: A System for Recommending OERs in a MOOC

Abstract

Personalization in the field of Technology Enhanced Learning (TEL) is a topic that received a lot of concern by researchers. At the same time, there is a growing amount of Open Educational Resources (OER) indexed according to the W3C standards. Relevant OERs can usefully complement the contents delivered to a learner during an online course. Computing the best OERs to offer to the learner at each point of his course is an aspect of personalization that we address in this paper. We designed our MORS system to solve this problem in the context of Massive Open Online Courses (MOOC). Our MORS system described in this paper, is based on a learner profile, on metadata describing the course and on a carefully crafted process to query the SparQL endpoints for OERs.