Research on optimization and practice of personalized learning path driven by data
数据驱动的个性化学习路径优化与实践研究

摘要

随着信息技术在教育领域的深入应用，个性化学习成为教育发展的重要趋势。本文聚焦于数据驱动的个性化学习路径优化，详细阐述了其理论基础、模型构建、算法设计以及实践应用等方面。通过对多源学习数据的收集与分析，构建精准的学习者模型和课程知识网络模型，运用智能算法为学习者推荐个性化学习路径，并在实践中不断优化和完善。研究结果表明，数据驱动的个性化学习路径能够显著提升学习效率和学习体验，为教育教学的创新发展提供了有力支持。

Abstract

With the deepening integration of information technology in education, personalized learning has emerged as a pivotal trend in educational development. This paper focuses on data-driven optimization of personalized learning pathways, detailing its theoretical foundations, model construction, algorithm design, and practical applications. Through collecting and analyzing multi-source learning data, we establish precise learner models and course knowledge network models. Intelligent algorithms are then employed to recommend customized learning paths for learners, with continuous refinement through practical implementation. The research findings demonstrate that data-driven personalized learning pathways significantly enhance learning efficiency and experience, providing robust support for innovative educational development.