Development of an Ontology-Based Personalised E-Learning Recommender System


  • Oluwatoyin Catherine Agbonifo Department of Information Systems, Federal University of Technology, Akure, Nigeria
  • Motunrayo Akinsete Department of Computer Science, Federal University of Technology, Akure, Nigeria


Personalised, E-learning, Recommender, Ontology, Collaborative filtering


E-learning has become an active field of research with a lot of investment towards web-based delivery of personalised learning contents to learners. Some issues of e-learning arise from the heterogeneity and interoperability of learning content to suit learner’s style and preferences in order to improve the e-learning environment. Hence, this paper developed an ontology-based personalised recommender system that is needed to recommend suitable learning contents to learners using collaborative filtering and ontology. A pre-test is carried out for users in order to segment them in learning categories to suit their skill level. The learning contents are structured using ontology; and collaborative filtering is used to collects preferences from many users and then recommending the highest rated contents to users. The system is implemented using JAVA programming language with Structured Query Language (MySQL) as database management system. Performance evaluation of the system is carried out using survey and standard metrics such as precision, recall and F1-Measrure. The results from the two performance evaluation models showed that the system is suitable for recommending the required learning contents to learners.


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How to Cite

Agbonifo, O. C. ., & Akinsete, M. . (2020). Development of an Ontology-Based Personalised E-Learning Recommender System. International Journal of Computer (IJC), 38(1), 102–112. Retrieved from