Analytics 2.0 for Precision Education Driven by Knowledge Map

Abstract

Aiming to solve learning difficulties caused by information explosion in the current era of education informatization, this paper proposes a precise teaching model driven by knowledge graphs. Knowledge graphs are essentially learning tools that effectively build a correct and complete curriculum knowledge system and accurately promote personalized learning paths. The main research contents are: (1) In view of the problem that the general knowledge graphs currently in use are not applicable in the field of education, we define the knowledge graph ontology structure of special courses based on Bloom’s teaching target system; (2) In view of the diversification of various teaching data sources, a simple knowledge graph representing structured data is used as a heuristic condition to extract a complete teaching sequence between knowledge points through self-expansion. (3) In view of the problem faced when trying to navigate relevant knowledge points in personalized learning, this paper exploits the internal relationship between knowledge point loopholes and ability achievements, and constructs an accurate path recommendation model based on quantitative data analysis. Taking the C language programming course as experimental data, this paper verifies the effectiveness of this model by quantitative means, which can significantly provide accurate teaching quality and realize the "multi-directional adaptation" among teachers, courses and students.