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.

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[1] Yong-Wei Zhang, "0000-0002-7732-4033", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/kdv5-7m88. Accessed: Jun. 14, 2024.
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Yong-Wei Zhang. (2024). 0000-0002-7732-4033. IEEE Dataport. http://dx.doi.org/10.21227/kdv5-7m88
Yong-Wei Zhang, 2024. 0000-0002-7732-4033. Available at: http://dx.doi.org/10.21227/kdv5-7m88.
Yong-Wei Zhang. (2024). "0000-0002-7732-4033." Web.
1. Yong-Wei Zhang. 0000-0002-7732-4033 [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/kdv5-7m88
Yong-Wei Zhang. "0000-0002-7732-4033." doi: 10.21227/kdv5-7m88