Personalized Learning Resource Recommendation Based on Course Ontology and Cognitive Ability

Personalized Learning Resource Recommendation Based on Course Ontology and Cognitive Ability

As an important way to solve the learner’s information loss within an e-learning system and support personalized learning, learning resource recommendation has attracted more and more attention. A personalized learning resource recommendation framework based on course ontology and the learner’s cognitive ability is proposed in this paper. Firstly, course ontology is constructed with C language course as an example, and semantics reasoning rules are defined based on course ontology. Then, according to the test results, the learner’s cognitive ability is dynamically estimated using maximum likelihood estimation and joint probability, and a learner model based on learning preference and cognitive ability is constructed. Finally, it explores the personalized learning resource recommendation method that integrates course ontology, the learner’s cognitive ability and learning preference. In the experimental part, the proposed recommendation method is applied to the e-learning system, and the experiment is carried out in the C language course teaching to verify the feasibility and effectiveness of the proposed recommendation method.