A Novel Two-Stage Personalized Learning Path Recommendation Approach for E-Learning

In the context of e-learning, learners often struggle to make informed decisions about what and how to learn when they have access to a vast array of learning resources. To address this issue, several approaches have been proposed from different perspectives, aiming to generate personalized learning paths for e-learners. These approaches include learner-based, knowledge-based, and hybrid recommendation methods. Among them, hybrid methods have emerged as a promising solution for personalized learning path recommendations, as they combine the strengths of both learner-based and knowledge-based approaches. However, existing hybrid methods typically employ exhaustive techniques to determine optimal paths by extracting all possible learning paths. This approach can be time-consuming and computationally expensive. To overcome this challenge, we propose a novel two-stage personalized learning path recommendation approach that integrates concept map and an improved genetic algorithm. We conducted computational experiments using diverse simulation datasets to assess the effectiveness of our proposed method. The experimental results indicate that our approach surpasses other competing methods in terms of both performance and robustness.