基于知识图谱的高校个性化学习系统研究

Research on a Personalized Learning System for Higher Education Based on Knowledge Graphs

摘要: 随着国家教育数字化战略持续推进，人工智能、大数据、知识图谱等技术正在深刻改变高校教学组织方式。高校数字化学习平台虽然积累了大量课程资源和学习数据，但仍存在资源匹配不精准、学习诊断不深入、教学工具协同不足、评价偏重结果等问题。知识图谱具有语义关联、结构化表达和知识推理能力，能够将课程知识点、学习资源、学习任务、评价标准和学生学习行为进行系统整合。本文围绕高校个性化学习需求，提出基于知识图谱的高校个性化学习系统框架，从学习资源重组、课程知识建模、D-KG-AI技术基座、学习者画像、学习诊断、路径推荐、教学应用场景和数据伦理保障等方面展开分析。研究认为，基于知识图谱的个性化学习系统能够推动高校教学从统一化、经验化向精准化、动态化和智能化转型，为高等教育数字化改革提供新的技术路径和实践思路。同时，本文进一步分析该系统在技术可靠性、算法偏见、数据隐私、教师技术能力和学生自主探索等方面可能面临的挑战与局限。

Abstract: With the continuous advancement of the national education digitalization strategy, technologies such as artificial intelligence, big data, and knowledge graphs are profoundly transforming the organization of teaching in higher education. Although digital learning platforms in universities have accumulated abundant course resources and learning data, they still face several problems, including inaccurate resource matching, insufficient learning diagnosis, weak coordination among teaching tools, and an overemphasis on outcome-based evaluation. Knowledge graphs, with their capabilities of semantic association, structured representation, and knowledge reasoning, can systematically integrate course knowledge points, learning resources, learning tasks, assessment standards, and students’ learning behaviors. Focusing on the needs of personalized learning in higher education, this paper proposes a knowledge graph-based framework for personalized learning systems in universities. It analyzes the system from the perspectives of learning resource reorganization, course knowledge modeling, the D-KG-AI technological foundation, learner profiling, learning diagnosis, learning path recommendation, application scenarios, and data ethics safeguards. The study argues that a knowledge graph-based personalized learning system can promote the transformation of higher education from standardized and experience-based teaching toward precise, dynamic, and intelligent teaching, providing a new technical pathway and practical approach for the digital reform of higher education. The paper also examines the challenges and limitations that may arise during implementation, including technical reliability, algorithmic bias, data privacy, teachers’ digital competence, and the possible weakening of students’ autonomous exploration.