Personalized Learning Path Recommendation Based on Learner Profile and Knowledge Graph

Abstract

E-learning is increasingly popular because it allows learners to freely choose their class times and locations. However, traditional E-learning platforms face issues of information overload and fragmented resources. The proposition of the concept of personalized learning has effectively alleviated these problems. However, current personalized learning recommendation methods fail to comprehensively and systematically address learners' needs. To solve this issue, this paper proposes a learning path recommendation method based on learner profiles. First, by collecting learners' personal information, learning history, and behavior data, a learner profile is established considering multiple aspects. Then, generating a path evaluation function for learners from the profile. Using the Ant Colony Optimization algorithm, the most suitable personalized learning path for the learner's needs is searched within the knowledge graph. Experimental results demonstrate that the personalized learning path recommendations generated by our algorithm meet expectations and achieve the best overall performance in comparative experiments.