基于知识图谱的个性化编程教学研究

Personalized Programming Teaching Driven by Knowledge Graphs

摘要: 在人工智能与教育数字化深度融合的背景下，高校编程教育面临教学模式单一、课程体系更新滞后、个性化支持不足等挑战。为响应国家教育数字化战略，提升编程教学质量，本文提出基于知识图谱的个性化编程教学路径构建机制。通过系统梳理编程知识点，构建结构化知识图谱，结合学习者画像与智能推荐算法，实现学习路径的动态生成与个性化推送。同时，融合混合式教学设计理念，推动教学内容、方法与评价方式的协同优化。研究表明，该机制有助于提升学生的学习主动性与编程能力，推动高校编程教育向智能化、精准化方向发展。

Abstract: Against the backdrop of deep integration between artificial intelligence and educational digitalization, programming education in higher education faces challenges such as uniform teaching modes, outdated curriculum systems, and insufficient personalized support. In response to national strategies for educational digitalization and to improve the quality of programming instruction, this paper proposes a personalized programming teaching mechanism based on knowledge graphs. By systematically organizing programming knowledge points and constructing structured knowledge graphs, combined with learner profiling and intelligent recommendation algorithms, the mechanism enables dynamic generation and personalized delivery of learning paths. Furthermore, it integrates blended teaching design to promote coordinated optimization of teaching content, methods, and assessment. The study shows that this approach enhances students’ learning initiative and programming competence, and supports the intelligent and precise development of programming education in universities.