Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum

Authors

  • Hongwei Yue Guangdong University of Education
  • Hanhui Lin Guangdong University of Finance and Economics;South China Normal University
  • Yingying Jin Guangzhou Panyu Polytechnic
  • Hui Zhang Guangdong University of Education
  • Ken Cai Zhongkai University of Agriculture and Engineering

DOI:

https://doi.org/10.3991/ijet.v17i14.32613

Keywords:

artificial intelligence, KG of curriculums, university education, educational informationization

Abstract


The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.

Cover iJET Vol. 17 No. 14 (2022)

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Published

2022-07-26

How to Cite

Yue, H. ., Lin, H., Jin, Y. ., Zhang, H. ., & Cai, K. . (2022). Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum. International Journal of Emerging Technologies in Learning (iJET), 17(14), pp. 64–77. https://doi.org/10.3991/ijet.v17i14.32613

Issue

Vol. 17 No. 14 (2022)

Section

Papers

License

Copyright (c) 2022 Hongwei Yue, Hanhui Lin, Yingying Jin, Hui Zhang, Ken Cai

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