Work

Knowledge Graph-Assisted Learning: Improving Academic Outcomes and Equity in Engineering Fluid Mechanics

Year: 2025

Type: article

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.

Source: Advances in Educational Technology and Psychology

DOI: 10.23977/aetp.2025.090401

Authors: Juan Fu, Zhenhuan Ye, Ming Lv, Di Tang

Institution: Beijing Jiaotong University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $329