Data: Optimizing Personalized Learning in Online Education through Integrated Cross-Course Learning Path Planning

This study investigates the optimization of cross-course learning paths in e-learning environments, addressing the challenge of navigating vast educational resources and aligning them with diverse learner needs. We propose a novel cross-course learning path planning model that integrates resources from multiple courses to tailor educational experiences to individual learner profiles. The model employs a modified affinity function, the item response theory (IRT), and a knowledge graph to effectively match learners' abilities with material difficulties and prerequisites. We introduce an innovative variable-length continuous representation (VLCR) to ensure the uniqueness of learning paths, combined with a differential evolution algorithm to optimize the multi-attribute matching (MAM) model, enhancing the personalization of the learning experience. Our numerical experiments confirm the model's efficacy in reducing educational mismatches and improving learning outcomes. This research contributes a significant framework for developing adaptive e-learning systems that cater to the evolving demands of learners in digital education platforms.

本研究聚焦电子学习（e-learning）环境中的跨课程学习路径优化问题，旨在应对海量教育资源检索困难、且难以适配多样化学习者需求的挑战。本研究提出一种新颖的跨课程学习路径规划模型，该模型整合多门课程的教育资源，可为不同学习者画像定制个性化学习体验。该模型采用改进型亲和度函数、项目反应理论（Item Response Theory, IRT）以及知识图谱（Knowledge Graph），可有效将学习者能力与学习材料的难度及先修要求进行匹配。本研究引入一种创新的变长连续表征（Variable-length Continuous Representation, VLCR）以确保学习路径的唯一性，并结合差分进化算法对多属性匹配（Multi-attribute Matching, MAM）模型进行优化，进一步提升学习体验的个性化程度。本研究通过数值实验验证了该模型在减少教育适配偏差、提升学习成效方面的有效性。本研究为适配数字教育平台中学习者不断变化的需求提供了一套极具价值的自适应电子学习系统开发框架。