人工智能赋能地方高校分析类课程教学改革和实践

AI-Empowered Teaching Reform and Practice of Analysis Courses in Local Universities

摘要: 以知识图谱为枢纽，贯通教与学全过程，解决分析类课程群抽象概念难理解、知识关联性弱、高阶思维培养难等问题，通过构建与应用课程知识图谱，强化逻辑思维与数据思维的深度融合。“思维融合”即有机融入数据思维于分析类课程群教学体系，基于知识图谱技术系统梳理课程关键知识点的内在关联与逻辑脉络，构建可视化教学资源。知识图谱的应用显著提升了学生对课程内容的理解与内化效率，同步强化了其数据分析和逻辑推理能力，有效实现了思维模式的融合创新。“数智协同”即深度融合机理建模与数据驱动方法，构建混合建模典型案例库，引导学生通过案例解析掌握结合机理分析与数据建模解决实际问题的双重优势，显著提升综合应用与实践能力。同步将大数据与人工智能领域的前沿技术案例及思政元素有机融入课程设计，协同强化学生的数智素养与价值引领。“思维融合”与“数智协同”数学与人工智能技术的协同教学模式为分析类课程教学提供新的教学范式。

Abstract: Using the knowledge graph as a hub to connect the entire teaching and learning process, this approach addresses key challenges in analysis course clusters, such as the difficulty of understanding abstract concepts, weak knowledge associations, and the cultivation of higher-order thinking. By constructing and applying course knowledge graphs, it strengthens the deep integration of logical thinking and data thinking. “Integration of Thinking” refers to the organic incorporation of data thinking into the teaching system of analysis course clusters. It systematically sorts out the intrinsic connections and logical relations of key knowledge points based on knowledge graph technology, constructing visual teaching resources. The application of knowledge graphs significantly enhances students’ understanding and internalization efficiency of course content, simultaneously strengthens their data analysis and logical reasoning abilities, and effectively achieves the integrated innovation of thinking models. “Digital-Intelligence Collaboration” refers to the deep integration of mechanism modeling and data-driven methods to build a typical case library of hybrid modeling. It guides students to grasp the dual advantages of combining mechanistic analysis with data modeling for solving practical problems through case analysis, significantly improving their comprehensive application and practical abilities. Meanwhile, it organically integrates cutting-edge technology cases and ideological and political elements from the fields of big data and artificial intelligence into the course design, synergistically strengthening students’ digital intelligence literacy and value guidance. The collaborative teaching model of “Integration of Thinking” and “Digital-Intelligence Collaboration”, which combines mathematics and artificial intelligence technology, provides a new teaching paradigm for analysis course instruction.