This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a scalable approach for creating adaptive learning paths within E-learning systems. The ALPN system employs an attention-based Knowledge Tracing (AKT) model to evaluate students' knowledge states and a decision-making model using Proximal Policy Optimization (PPO) to suggest customized learning materials. The proposed system accommodates students' needs by considering personalization parameters such as learning objectives, time constraints, and knowledge backgrounds. Through an iterative process of recommendation and knowledge state updating, the ALPN system produces highly adaptive learning paths. Experimental results reveal the outstanding performance of the proposed system, providing good insights into the future development of E-learning systems.

翻译：本文介绍了自适应学习路径导航（ALPN）系统，该是一种可扩展的方法，用于在电子学习系统中创建自适应学习路径。ALPN系统采用基于注意力的知识追踪（AKT）模型评估学生的知识状态，并利用近端策略优化（PPO）构建决策模型，以推荐定制化的学习材料。该系统通过考虑学习目标、时间约束和知识背景等个性化参数，满足学生的需求。通过推荐与知识状态更新的迭代过程，ALPN系统生成了高度自适应的学习路径。实验结果表明，该系统具有优异性能，为电子学习系统的未来发展提供了重要启示。