Zhang Shiyao, Wang Caixia, Gao Qiyue

School of Computer and Software Engineering, University of Science and Technology Liaoning

Abstract：

With the development of artificial intelligence and big data technologies, Python has become an important instructional language for programming courses in non-computer majors at universities. However, traditional laboratory teaching faces issues such as fragmented content, a single assessment mechanism, and case studies detached from professional contexts, making it difficult to effectively cultivate students’ programming thinking and comprehensive practical abilities. This paper proposes a reform model for Python laboratory teaching that integrates knowledge graphs and competency graphs. By constructing a structured network of knowledge points and a quantifiable learning ability development path, the model achieves systematic teaching content, visualized learning processes, and diversified evaluation mechanisms. The reform measures include designing a progressive experimental content system, developing professional-related experimental cases, implementing project-driven teaching, building an intelligent laboratory platform, and introducing a competency self-assessment mechanism that promotes learning through evaluation. Practice shows that this model significantly enhances students’ learning interest, problem-solving skills, and self-directed learning awareness, providing a replicable paradigm for teaching reforms in programming courses at universities.

Key Words：

Python laboratory teaching; knowledge graph; competency graph; teaching reform; non-computer majors