摘 要：随着信息技术的迅猛发展，大数据技术为教育领域带来了前所未有的机遇，个性化学习路径推荐系统成为提升学习效率和体验的重要手段本研究旨在设计并实现一种基于大数据分析的个性化学习路径推荐系统，通过整合学习者的行为数据、知识图谱及学习目标，构建精准的学习路径模型研究采用数据挖掘与机器学习相结合的方法，对大规模学习行为数据进行深度分析，提取用户特征和学习规律，并结合知识图谱技术生成动态适应性的学习路径实验结果表明，该系统能够显著提高学习者的知识掌握程度和学习满意度，相较于传统推荐方法，其准确性和个性化程度均有明显提升本研究的主要创新点在于将多源异构数据融合与动态知识图谱更新机制引入推荐系统，从而实现了更贴合个体需求的学习路径规划，为智能化教育提供了新的思路和实践参考
关键词：个性化学习路径；大数据分析；知识图谱；机器学习；学习行为数据
Design and Application of a Personalized Learning Path Recommendation System Based on Big Data
英文人名
Directive teacher:×××
Abstract：With the rapid development of information technology, big data technology has brought unprecedented opportunities to the education field, and personalized learning path recommendation systems have become an important means to improve learning efficiency and experience. This study aims to design and implement a personalized learning path recommendation system based on big data analysis, which integrates learners' behavioral data, knowledge graphs, and learning ob jectives to construct precise learning path models. By combining data mining with machine learning approaches, this research conducts in-depth analysis of large-scale learning behavior data to extract user characteristics and learning patterns, while leveraging knowledge graph technology to generate dynamically adaptive learning paths. Experimental results demonstrate that the system significantly enhances learners' knowledge acquisition and learning satisfaction, showing marked improvements in accuracy and personalization compared to traditional recommendation methods. The primary innovation of this study lies in incorporating multi-source heterogeneous data fusion and a dynamic knowledge graph updating mechanism into the recommendation system, thereby achieving more individualized learning path planning and providing new insights and practical references for intelligent education.
Keywords: Personalized Learning Path；Big Data Analysis；Knowledge Graph；Machine Learning；Learning Behavior Data
目 录
引言 1
一、大数据与学习路径推荐系统概述 1
(一)大数据技术在教育中的应用 1
(二)个性化学习路径推荐的意义 2
(三)推荐系统的核心技术分析 2
二、系统设计的关键要素与框架构建 2
(一)学习者特征的数据建模 3
(二)推荐算法的选择与优化 3
(三)系统架构的设计与实现 4
三、数据处理与学习路径生成策略 4
(一)数据采集与预处理方法 4
(二)基于用户行为的学习路径生成 5
(三)动态调整机制的设计与应用 5
四、系统应用效果评估与改进方向 5
(一)应用场景的案例分析 6
(二)效果评估指标体系构建 6
(三)系统优化与未来展望 6
结论 7
参考文献 8
致谢 8
