知识图谱驱动的《产业经济学》智能化教学模式构建与实施路径研究

Research on the Construction and Implementation Path of an Intelligent Teaching Model for Industrial Economics Driven by Knowledge Graphs

摘要: 人工智能、大数据等数字技术的发展推动高校课程教学模式加速转型。针对《产业经济学》课程知识关联复杂、传统教学中知识体系呈现碎片化、个性化学习支持不足等问题，本文基于建构主义学习理论、认知负荷理论和精准教学理论，探讨知识图谱驱动课程智能化教学改革的作用机制，构建了“知识体系重构–教学资源智能组织–学习支持与评价优化”的智能化教学模式，并提出课前智能诊断、课中知识关联、课后动态反馈的实施路径。研究表明，知识图谱能够通过优化课程知识组织方式、促进教学资源融合和支持精准化学习，推动《产业经济学》教学由知识传递向知识建构、由统一教学向个性化支持转变，为经管类课程数字化教学改革提供参考。

Abstract: The development of artificial intelligence and big data technologies has accelerated the transformation of teaching models in higher education. Addressing the problems of fragmented knowledge organization and insufficient personalized learning support in Industrial Economics courses, this study explores the mechanism of knowledge graph-driven intelligent teaching reform based on constructivist learning theory, cognitive load theory, and precision teaching theory. An intelligent teaching model featuring “knowledge system reconstruction, intelligent organization of teaching resources, and optimization of learning support and assessment” is constructed, with implementation paths proposed from pre-class intelligent diagnosis, in-class knowledge association, and post-class dynamic feedback. The study indicates that knowledge graphs can promote the transformation of Industrial Economics teaching from knowledge transmission to knowledge construction and from standardized instruction to personalized learning support by optimizing the organization of course knowledge, facilitating the integration of teaching resources, and enabling precision learning, thereby providing practical references for digital curriculum reform in economics and management education.