A Personalized Learning Path Recommendation Method for Learning Objects with Diverse Coverage Levels

E-learning has resulted in the proliferation of educational resources, but challenges remain in providing personalized learning materials to learners amidst an abundance of resources. Previous personalized learning path recommendation (LPR) methods often oversimplified the competency features of learning objects (LOs), rendering them inadequate for LOs with diverse coverage levels. To address this limitation, an improved learning path recommendation framework is proposed that uses a novel graph-based genetic algorithm (GBGA) to optimize the alignment of features between learners and LOs. To evaluate the performance of the method, a series of computational experiments are conducted based on six simulation datasets with different levels of complexity. The results indicate that the proposed method is effective and stable for solving the LPR problem using LOs with diverse coverage levels.