Author(s)

Juan Fu ¹, Zhenhuan Ye ¹, Ming Lv ², Di Tang ¹

Affiliation(s)

¹ School of Engineering, Zunyi Normal University, Zunyi, 563006, China
² School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

Corresponding Author

Juan Fu

ABSTRACT

To address the challenges of abstract concepts, complex derivations, and polarized academic performance in Engineering Fluid Mechanics, this study introduces the Engineering Fluid Mechanics Knowledge Graph (EFM-KG), grounded in cognitive load and meaningful learning theories. A quasi-experimental design with non-equivalent control groups compared the final examination scores of an EFM-KG-assisted group (n=51) with a traditional instruction group (n=89). The experimental group’s mean score increased by 15.0% (9.53 points), with a 28.1% reduction in standard deviation and a large effect size (Cohen’s d=0.80). These findings confirm EFM-KG’s effectiveness in enhancing academic performance and promoting educational equity by narrowing achievement gaps. This research provides robust evidence for knowledge graphs in engineering education and insights for optimizing instructional design.

KEYWORDS

Knowledge Graph, Engineering Education, Academic Performance, Quasi-Experimental Study, Cognitive Load

CITE THIS PAPER

Juan Fu, Zhenhuan Ye, Ming Lv, Di Tang, Knowledge Graph-Assisted Learning: Improving Academic Outcomes and Equity in Engineering Fluid Mechanics. Advances in Educational Technology and Psychology (2025) Vol. 9: 1-8. DOI: http://dx.doi.org/10.23977/aetp.2025.090401.

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• Curriculum and Teaching Methodology
  
  
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