Educational Technologies

Application and evaluation of teaching effects of knowledge graph in promoting deep learning in epidemiology

  • Luo Yingyi ,
  • Li Jia ,
  • Sun Wenwen ,
  • Sheng Yueying ,
  • Weng Huachun ,
  • Chen Yanfeng
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  • Teaching Department of Inspection and Quarantine, College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China

Received date: 2025-03-28

  Online published: 2026-03-27

Abstract

Objective To investigate the application and teaching effects of Knowledge Graph (KG) in promoting deep learning in epidemiology course, and to provide references for mathematical intelligence curriculum reform. Methods From September 2024 to December 2024, undergraduate students majoring in health inspection and quarantine enrolled in 2022 were selected as the research participants. An online KG teaching module was used to assist in the teaching process of Epidemiology course. Information including grade point averages, learning attitudes, learning habits, as well as the learning effects of KG on their deep learning were collected through online questionnaires. Multiple linear regression analysis was used to study the influencing factors of students′ deep learning effects and the process and final assessment scores. Results The KG learning effect score of was 90.0 (12.5), and the self-assessment score of deep learning effect was 36.0 (7.2). The process assessment score was 88.0 (10.5), and the final assessment score was 71.5 (32.5). Univariate analysis found that students with characteristics such as long daily review time after class, high grade point average in the previous semester, strong exploration ability, and strong ability to apply knowledge to practice had better learning effects with the knowledge graph (all P<0.05). Multiple linear regression analysis indicated that students′ knowledge graph learning effect was a positive factor for their self-assessment of deep learning (P=0.010). The knowledge graph learning effect score was a positive influencing factor for process assessment scores and final assessment scores (P<0.001; P=0.042). Strong exploration ability of students promoted the improvement of process assessment scores and final assessment scores (P=0.021; P=0.032). Conclusions Integrating KG into the teaching of Epidemiology courses could guide the students to cultivate personalized learning characteristics, including deep learning abilities and exploration abilities, and enhance the deep learning effects and teaching quality in mathematical intelligence curriculum reforms.

Cite this article

Luo Yingyi , Li Jia , Sun Wenwen , Sheng Yueying , Weng Huachun , Chen Yanfeng . Application and evaluation of teaching effects of knowledge graph in promoting deep learning in epidemiology[J]. Chinese Journal of Medical Education, 2026 , 46(4) : 275 -279 . DOI: 10.3760/cma.j.cn115259-20250328-00340

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