This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students. The ALPN system integrates the Attentive Knowledge Tracing (AKT) model, which assesses students' knowledge states, with the proposed Entropy-enhanced Proximal Policy Optimization (EPPO) algorithm. This new algorithm optimizes the recommendation of learning materials. By harmonizing these models, the ALPN system tailors the learning path to students' needs, significantly increasing learning effectiveness. Experimental results demonstrate that the ALPN system outperforms previous research by 8.2% in maximizing learning outcomes and provides a 10.5% higher diversity in generating learning paths. The proposed system marks a significant advancement in adaptive E-learning, potentially transforming the educational landscape in the digital era.

翻译：本文介绍了一种名为自适应学习路径导航（ALPN）的系统，该创新方法通过为学生提供高度自适应的学习路径，增强了在线学习平台的功能。ALPN系统将评估学生知识状态的注意力知识追踪（AKT）模型与本文提出的熵增强近端策略优化（EPPO）算法相结合，后者用于优化学习材料的推荐。通过协调这些模型，ALPN系统能够根据学生需求定制学习路径，从而显著提升学习效果。实验结果表明，与先前研究相比，ALPN系统在最大化学习成果方面提升了8.2%，并在生成学习路径的多样性上提高了10.5%。该系统标志着自适应在线学习领域的一大进步，有望在数字时代变革教育格局。