Content-based recommendation system improves learning experience in MOOCs, highlighting the role of semantic ontology.
With the rapid expansion of Massive Open Online Courses (MOOCs), learners face increasing challenges in identifying relevant educational content tailored to their interests and needs. This paper presents the design and implementation of a content-based recommendation system (CBRS) for Coursera courses, enhanced through the use of Semantic Web and ontology technologies. We constructed a domain-specific OWL ontology using real-world data extracted from over 1,000 Coursera courses, capturing key attributes. The system leverages semantic representations to improve the accuracy and relevance of recommendations by computing similarities between course contents and user preferences. The recommendation engine was evaluated using a test set of user queries and relevance judgments. Experimental results show strong performance, achieving Precision 0.98%, Recall 1.00, F1 Score ≈0.99% and Accuracy 0.98%. These findings demonstrate the system’s effectiveness in delivering personalized, high-quality recommendations and underscore the value of integrating ontologies into educational recommender systems. This work contributes to the advancement of intelligent e-learning systems by enhancing resource discoverability, user engagement, and overall learning experience in MOOC platforms.
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Mustafa et al. (2025) studied this question.