A Semantic Recommendation System for Learning Personalization in Massive Open Online Courses

Sara Assami, Najima Daoudi, Rachida Ajhoun


For an innovation producing education, MOOC (Massive Open Online Course) platforms offer a plethora of learning resources and pedagogical activities to support the university’s 4.0 new era and the lifelong learning movement. Nevertheless, the rapid advances in learning technologies imply the need for personalized guidance for learners and adapted learning materials. In this paper we seek to enhance the MOOC learner experience by providing a semantic recommender system for the diversity and abundance of MOOCs available for learners. Firstly, the paper analyses the state of the art of the semantic recommendation approach in a distance learning context. Then it describes the proposed MOOC recommendation system that uses the ontological representation of the learner model and MOOCs content to make its intelligent suggestions. Finally, we explore the development phases of the semantic MOOC recommendation system to define the implications for the progress of our research.


Distance learning, Massive Open Online Course, recommendation system, Knowledge-based recommendation

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International Journal of Recent Contributions from Engineering, Science & IT (iJES) – eISSN: 2197-8581
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