0000-0002-7732-4033

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.