Ideological-Political Dimensions in Knowledge Graphs: Student-Driven Association Discovery and Graph Optimization in Management Statistics

针对量化分析类课程中融入思政建设的问题，尤其是专业知识传授与思政要素渗透间存在机械拼接的突出问题，本研究致力于探索工具理性与价值理性融合的课程设计。通过探索构建知识图谱和思政图谱关联模型，旨在引导学生参与知识图谱和思政图谱的联合建设与优化，以回应新时代课程思政改革的实践需求。通过关联度评审机制和德尔菲法进行图谱关键点识别，并采用图谱优化提案竞赛加强学生参与，依托“优学院”等智慧教育平台构建学生们数字学习路径体系，凭借发布价值观反思日志任务和通过结构化设计的同辈互评机制，对学生学习过程进行有效追踪，以此驱动学生在知识储备与价值观念层面实现同步跃升。Addressing the issue of integrating ideological and political education construction into quantitative analysis courses, particularly the prominent problem of mechanical splicing between professional knowledge teaching and the infiltration of ideological and political elements, this study is dedicated to exploring curriculum design that integrates instrumental rationality and value rationality. By constructing an association model between knowledge graphs and ideological-political graphs, it aims to guide students in participating in the joint construction and optimization of knowledge graphs and ideological-political graphs, responding to the practical needs of ideological-political curriculum reform in the new era. Through relevance evaluation mechanisms and the Delphi method for identifying key points in the graphs and adopting graph optimization proposal competitions to strengthen student engagement, a digital learning path system for students is built on smart education platforms such as “U-Learning.” By assigning value reflection journal tasks and implementing a structured peer review mechanism, students’ learning processes are effectively tracked, thereby driving simultaneous advancement in both knowledge acquisition and value development.