知识图谱赋能高职学生个性化学习路径构建

Knowledge Graph-Empowered Personalized Learning Path Construction for Higher Vocational Students

摘要: 知识图谱技术为高职个性化学习提供了结构化支撑，但在实际落地中，平台的课程育人效果受到两个因素的明显制约：一是师生对平台有用性的感知偏弱，二是不同专业群之间的使用效果差异较大。本文以技术接受模型为分析框架，对298名高职学生进行问卷调查，并对12名教师进行半结构化访谈。结果显示：感知有用性对使用意愿的路径系数为0.43，是驱动平台使用的首要因素；高频使用组的学习路径适配满意度(3.91)显著高于低频组(3.18)；信息技术专业学生的使用意愿(3.74)明显高于现代农业专业学生(3.31)。基于上述发现，提出三条实践路径：以岗位能力为锚点构建“能力–知识–节点”三层图谱；在平台推广初期设计效果可见性环节以强化教师的感知有用性；建立测评结果自动回写图谱的诊断–推荐–反馈–迭代闭环，使知识图谱对高职课程育人的赋能真正落到学生个体层面。

Abstract: Knowledge graph technology provides structured support for personalized learning in higher vocational education. However, in practice, the effectiveness of curriculum-based education is constrained by two factors: weak perceived usefulness among teachers and students, and significant performance gaps across different professional clusters. Drawing on the Technology Acceptance Model (TAM) as the analytical framework, this study surveyed 298 higher vocational students and conducted semi-structured interviews with 12 teachers. The results indicate that perceived usefulness exerts the strongest effect on usage intention with a path coefficient of 0.43; students in the high-frequency usage group reported significantly higher learning path adaptation satisfaction (3.91) than those in the low-frequency group (3.18); students majoring in information technology showed notably stronger usage intention (3.74) compared to those in modern agriculture (3.31). Based on these findings, three practical pathways are proposed: constructing a competency-knowledge-node three-layer graph anchored to job capabilities; designing visibility-of-effect mechanisms during the initial promotion phase to strengthen teachers’ perceived usefulness; and establishing a closed-loop system of diagnosis-recommendation-feedback-iteration whereby assessment results are automatically written back to the knowledge graph, enabling knowledge graph empowerment for higher vocational education to genuinely reach in the individual student level.