Proceedings of the 3rd International Conference on Educational Development and Social Sciences (EDSS 2026)

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Practical Research on Knowledge Graph-Driven Intelligent Teaching Models: A Case Study of Local Applied Universities

Authors
Yingdong Xie1, Yan Guo1, *
1Department of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, China
*Corresponding author. Email: guoyan@oit.edu.cn
Corresponding Author
Yan Guo
Available Online 1 May 2026.
DOI
10.2991/978-2-38476-569-0_18How to use a DOI?
Keywords
Knowledge Graph; Intelligent Teaching; Teaching Reform; Local Applied Universities
Abstract

Local applied universities face a critical challenge: traditional teaching often delivers fragmented knowledge, failing to cultivate the systematic competencies required by industry. To address this, we propose and implement an intelligent teaching model centered on a domain-specific knowledge graph (KG). Our model integrates three layers—systematized knowledge, tiered competencies, and scenario-based literacy—into a dynamic closed-loop system that personalizes learning and provides actionable feedback. We constructed a KG for a Probability Theory course using entity-relationship-attribute triples extracted from multi-source data. An empirical study at Ordos Institute compared two cohorts taught by the same instructor with identical materials; one used our KG-driven model, the other traditional methods. Results showed the experimental group significantly outperformed the control group in final exam scores (68.7 vs. 61.8, p=0.007), with a notable 11.25-point advantage at the 25th percentile. This paper offers a practical, evidence-based blueprint for leveraging KG technology to bridge the gap between academic instruction and real-world application in resource-constrained settings.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Conference on Educational Development and Social Sciences (EDSS 2026)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
1 May 2026
ISBN
978-2-38476-569-0
ISSN
2352-5398
DOI
10.2991/978-2-38476-569-0_18How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yingdong Xie
AU  - Yan Guo
PY  - 2026
DA  - 2026/05/01
TI  - Practical Research on Knowledge Graph-Driven Intelligent Teaching Models: A Case Study of Local Applied Universities
BT  - Proceedings of the 3rd International Conference on Educational Development and Social Sciences (EDSS 2026)
PB  - Atlantis Press
SP  - 146
EP  - 152
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-569-0_18
DO  - 10.2991/978-2-38476-569-0_18
ID  - Xie2026
ER  -