A systematic review of ontology use in E-Learning recommender system

Abstract

Ontology and knowledge-based systems typically provide e-learning recommender systems. However, ontology use in such systems is not well studied in systematic detail. Therefore, this research examines the development and evaluation of ontology-based recommender systems. The study also discusses technical ontology use and the recommendation process. We identified multidisciplinary ontology-based recommender systems in 28 journal articles. These systems combined ontology with artificial intelligence, computing technology, education, education psychology, and social sciences. Student models and learning objects remain the primary ontology use, followed by feedback, assessments, and context data. Currently, the most popular recommendation item is the learning object, but learning path, feedback, and learning device could be the future considerations. This recommendation process is reciprocal and can be initiated either by the system or students. Standard ontology languages are commonly used, but standards for student profiles and learning object metadata are rarely adopted. Moreover, ontology-based recommender systems seldom use the methodology of building ontologies and hardly use other ontology methodologies. Similarly, none of the primary studies described ontology evaluation methodologies, but the systems are evaluated by nonreal students, algorithmic performance tests, statistics, questionnaires, and qualitative observations. In conclusion, the findings support the implementation of ontology methodologies and the integration of ontology-based recommendations into existing learning technologies. The study also promotes the use of recommender systems in social science and humanities courses, non-higher education, and open learning environments.