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As online learning resources increase exponentially on the World Wide Web, online students have difficulty choosing the most suitable and relevant learning material that meets their learning needs due to information overload. Online learning recommendation system is used to predict the preferences or ranking of learners' targets on learning objects for the purpose of generating recommendations. However, the Recommendation system is considered to lack the ability to resolve semantic interoperability issues with heterogeneous sources of information. The purpose of this study is to discuss the role of ontology in the development of recommendation systems in the online learning domain. There are four electronic journal databases selected as references, namely IEEE, Science Direct, Springer Link, and ACM Digital Library. This study obtained 9 articles that were synthesized to answer research questions. This study shows that the involvement of ontology for knowledge representation in the recommendation process can improve the accuracy and quality of recommendations and at the same time help to overcome the weaknesses associated with conventional recommendations.


Online Learning Ontology Recommender System

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