基于大学物理知识图谱的个性化教学研究与实践

Research and Practice on Personalized Teaching Based on Knowledge Graph of College Physics

摘要: 在促进人工智能助力教育变革领域，知识图谱在教育领域的应用越来越多元化和精细化，个性化的应用场景最多。本文立足教育数字化背景，以最近发展区、精准教学、差异化教学为支撑，依托大学物理课程知识图谱构建“AI学情诊断，ZPD定位，分层任务推送，多维评价复盘”的四环个性化教学模式。在教学改革实施后，证明基于大学物理知识图谱的个性化教学模式的可操作性。为同类大班基础课的数字化个性化改革提供参考。

Abstract: In the field of promoting artificial intelligence to empower educational transformation, knowledge graphs are being applied in increasingly diversified and refined ways in education, with personalized application scenarios being the most prevalent. Against the background of educational digitalization, this study, supported by the Zone of Proximal Development (ZPD) theory, precision teaching, and differentiated instruction, constructs a four-link personalized teaching model for College Physics based on the course knowledge graph, comprising “AI learning situation diagnosis, ZPD positioning, layered task delivery, and multi-dimensional evaluation review”. The implementation of the teaching reform verifies the operability of this knowledge-graph-supported personalized teaching model for College Physics, providing a reference for the digital personalized reform of similar large-class foundational courses.