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Research on Algorithm-driven Subject Knowledge Graphs Empowering Graduate Precision Teaching Mode

Author(s)

Xia Tao, Weiwei Huang, Shang Xu*

Affiliation(s)

School of Marxism Wuhan Institute of Technology, Wuhan, Hubei, China *Corresponding Author.

Abstract

The integration of AI and big data has revitalized precise teaching. Knowledge graphs, driven by algorithms, structure knowledge and integrate teaching resources, offering more precise content for graduate education. Applied to graduate teaching, they can solve the problems of generalized teaching content and difficulty in meeting individual student needs in traditional modes. This paper takes the "Principles of Education" course as an example. It builds and applies knowledge graphs, considers graduate teaching needs, and proposes a precise teaching model based on knowledge graphs. This model aims to optimize educational resource allocation and maximize teaching effectiveness.

Keywords

Knowledge Graph; Graduate Education; Precise Teaching; Teaching Model; Principles of Education

References

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