Ontology-Based Personalized Learning Path Recommendation for Course Learning

The personalized learning path is one of the most promising personalization solutions for e-learning. Previous work usually focuses on the learner preference which is not the most decisive factor in course learning. Therefore, we employ the closed-loop learning to propose an ontology-based learning path recommendation solution which includes an ontology-based learning path generation method and an update mechanism of the learner ontology. In particular, the generation method primarily utilizes learners' knowledge mastery rather than their preference to generate personalized learning path by considering the expertly designed learning order as the foundation. And due to the dynamic of the knowledge mastery, the update mechanism is proposed by simulating learners' actual knowledge growth process to keep this metadata up to date. Experimental results show that the recommendation system implemented with our solution has the potential to accurately simulate the actual knowledge growth process and continuously provide learners with satisfactory personalized learning paths for course learning.