知识图谱教育应用的结构性错位与认知转向

Structural Misalignment and Cognitive Turn in Educational Applications of Knowledge Graphs

摘要: 知识图谱正成为教育数字化基础设施的重要组成部分，在智能教学系统中的应用日益广泛。然而，当前研究过度聚焦于知识抽取算法、推荐准确率等技术指标优化，忽视了知识图谱作用于教与学的深层逻辑，导致“技术可行、落地困难”的普遍困境。本文采用理论分析方法，揭示这一困境的根源在于“知识工程逻辑”与“学习认知逻辑”的结构性错位，其本质是教学目标、认知规律与学习路径三重建模的缺失。基于此诊断，提出破解路径：从“学科知识图谱”走向“学生学科认知图谱”，以学科核心素养发展为目标，融合认知诊断模型与学习轨迹分析，将静态知识库转变为动态认知导航，实现从“技术可用”到“教育可用”的转变。

Abstract: Knowledge graphs are becoming an important part of the digital infrastructure of education and are increasingly being used in intelligent tutoring systems. However, current research has overemphasized the optimization of technical indicators such as knowledge extraction algorithms and recommendation accuracy, while neglecting the deeper logic of how knowledge graphs function in teaching and learning, resulting in a widespread dilemma of “technically feasible but difficult to implement”. Adopting a theoretical analysis approach, this paper reveals that the root cause of this dilemma lies in a structural misalignment between the “knowledge engineering logic” and the “learning-cognitive logic”, which essentially manifests as the absence of three levels of modeling: instructional objectives, cognitive laws, and learning paths. Based on this diagnosis, this paper proposes a pathway to address this dilemma: shifting from “subject knowledge graphs” to “student subject cognitive graphs”, with the goal of developing subject core competencies. By integrating cognitive diagnosis models with learning trajectory analysis, static knowledge repositories are transformed into dynamic cognitive navigators, enabling a transition from “technologically usable” to “educationally applicable”.