Ontology-Based Modeling for a Personalized MOOC Recommender System

Technology has revolutionized information access and influenced our learning habits. In this context, Massive Open Online Courses (MOOC) platforms emerged to satisfy the web user’s need for a lifelong learning. These platforms include research filters to help the learner find the right learning content, but the high dropout rates suggest the inadequacy of recommended MOOCs to learner needs. Hence, MOOC’s recommendation should reconsider the learner profile modeling to enlarge the scope of recommendation parameters. In this paper, we aim to choose the proper modeling technique for the personalization criteria used in a MOOC recommender system, such as the pace of learning and the cognitive learning style. For this purpose, an ontology-based modeling is used to structure the common concepts deduced from the learner profile and MOOC content. It is also a trace-based approach since it will take into consideration the learning history of a learner profile for an accurate MOOC recommendation.

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