Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review





online learning, personalization, course recommender systems, course features, learner profiles


As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

Author Biographies

Amir Narimani, Education and ICT (e-Learning), Open University of Catalonia (UOC), Barcelona, Spain

Amir Narimani is currently pursuing a Doctor of Philosophy (Ph.D.) in Education and ICT (e-learning) at the Open University of Catalonia (UOC). With a great interest in learning analytics and user modeling, he is committed to advancing the understanding of educational recommender systems through rigorous academic exploration. The thesis research focuses on grade prediction and learner satisfaction with the collaborative filtering course recommendations. In addition to his academic career, Amir is an enthusiastic practitioner of data-related business solutions and decision support systems.

Elena Barberà, Psychology and Education Sciences Studies, Open University of Catalonia (UOC), Barcelona, Spain

Doctor in Psychology from the University of Barcelona (1995). She is currently a full professor of the Studies of Psychology and Educational Sciences at the Universitat de Catalunya in Barcelona (Spain). Head of the Doctoral Program in ICT and Education at this university for ten years. Her research activity is specialized in the area of educational psychology, a field in which she has several publications, conferences, and educational courses, relating in particular to knowledge-construction processes and educational interaction in e-learning environments, evaluating educational quality, and assessing learning, distance learning using ICT and teaching and learning strategies.


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How to Cite

Narimani, A., & Barberà, E. (2024). Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review. The International Review of Research in Open and Distributed Learning, 25(1), 197–225. https://doi.org/10.19173/irrodl.v25i1.7419



Literature Reviews