Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning
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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.
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