With the development of artificial intelligence, the market demand for big data talents with both theoretical and practical competence has surged. Therefore, students' practical competence is crucial for the success of their programming learning. Currently, students majoring in big data face problems such as disconnection between skills and application scenarios, insufficient self-driven ability, and rationality deficits in AI application awareness, which restrict the improvement of their programming practical competence. Based on the Problem-Based Learning (PBL) theory, this study proposes four core strategies: first, enrich the diversity of teaching scenarios to guide students in exploring the essence of code and architectural design ideas, thereby consolidating the foundation of programming; second, implement a research-based learning model with the role transformation of dominant participants, enabling students to understand the underlying logic of code from multiple perspectives; third, construct the rational application of AI to cultivate students' critical thinking and reconstruction abilities regarding AI-generated code; fourth, encourage the openness of teaching materials and knowledge integration to facilitate students' autonomous learning and knowledge combing. Meanwhile, curriculum improvement strategies are put forward, allowing students to solve programming problems through multiple methods and introducing challenging real questions from competitions such as the Blue Bridge Cup. Practice has shown that these strategies can effectively stimulate students' sense of exploration, promote their transformation from passive learning to active knowledge construction, and provide references for programming teaching and innovative talent cultivation in big data majors.

Zhou Ruan, Na Xie, Chengqiang Wang, Zhenfen Dong. Exploration and Practice of Teaching Reform in Practical Courses for Big Data Majors Based on Problem-Based Learning—Taking the Course "Python Programming" as an Example. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 222-230. https://doi.org/10.25236/FER.2026.090530.

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