The Construction of Learning Diagnosis and Resources Recommendation System Based on Knowledge Graph

Abstract

With the deepening integration of artificial intelligence, ICT in education is approaching to the stage of smart education, the main purpose of which is to realize learning personalization. This paper constructs an intelligent tutoring system to allow teacher establish the course knowledge model visually based on ontology. This system evaluates the learning situation of students using a test auto-generated by a global prediction accuracy optimization algorithm. The learning diagnosis module is implemented according to the learning situations of students and the structure analysis of knowledge graph based on node contribution. The resource recommendation module is implemented through the importance ranking of learning resources. The prototype system is constructed and the experiments are conducted. The results show that our approach can achieve personalized learning well in a certain range.