Personalization criteria for enhancing learner engagement in MOOC platforms

The use of ICT (Information and Communication Technology) revolutionized the education sector with MOOCs (Massive Open Online Courses) becoming a major focus for open education actors and top universities to resolve the limited resources issue and make education accessible worldwide. Our study focuses on the problem of dropouts in MOOCs and how we can reduce the phenomenon by adapting learning according to criteria that enhance MOOC learners' motivation. It relies on enhancing the recommendation process of Courses according to the learner personal expectations, preferences and cognitive learning style among other personalization criteria. Furthermore, it suggests a new approach to profile construction for a MOOC recommendation system by considering external data to define a learner's interests.