Teaching Reform and Exploration of a Python Programming Course on the basis of a Knowledge Graph - Liang, Jinliang (Journal of Theory and Practice in Education and Innovation, 2025)
This paper explores the application of knowledge graphs in the reform and exploration of Python programming education, using a case study from Nanfang College Guangzhou. The study investigates the impact of knowledge graphs on student learning outcomes in a Python programming course, comparing an experimental group (EG) that utilized an interactive knowledge graph-based learning tool with a control group (CG) that followed traditional teaching methods. A mixed-methods approach was adopted, combining quantitative assessments (pre- and post-course quiz, final exams, and practical coding assignments) with qualitative feedback from students through surveys. The results reveal that the EG outperformed the CG in all assessment categories, showing a significant increase in quiz scores, final exam performance, and practical coding assignments. Specifically, the EG demonstrated a 13% improvement in quiz scores, a 15% increase in final exam scores, and an 18% improvement in coding assignments compared to the CG. Statistical analysis confirmed the significance of these differences, with p-values below 0.05 for all measures. Qualitative feedback from the EG also highlighted the effectiveness of the knowledge graphs in enhancing understanding of abstract programming concepts, improving problem-solving skills, and boosting confidence in applying Python programming to real-world problems. These findings suggest that knowledge graphs can serve as a powerful teaching tool in programming education, offering students a visual and interactive method to comprehend complex relationships between programming concepts. The study highlights the potential for integrating KGs into computer science curricula to foster deeper learning, reduce cognitive load, and improve student outcomes. Further research is recommended to explore the long-term impact of knowledge graphs on programming education and their applicability across different programming languages and educational contexts.