Integrating Reinforcement Learning with Dynamic Knowledge Tracing for personalized learning path optimization.

ABSTRACT: Personalized learning systems aim to improve student engagement and outcomes by adapting to individual learning needs. Traditional models, however, struggle to handle the dynamic nature of student learning and task sequencing. Knowledge Tracing (KT) is foundational for predicting student performance, but existing approaches lack the flexibility to account for evolving student knowledge. We propose RL-DKT, a novel framework that integrates Dynamic Knowledge Tracing (DKT) with Reinforcement Learning (RL). While DKT tracks the temporal evolution of a student's knowledge state, RL dynamically selects tasks based on individual performance, optimizing the learning path. The RL agent adapts task difficulty in real-time to ensure maximum retention and engagement. We evaluate the RL-DKT framework u

SUBMITTER: Fu Z 

PROVIDER: S-EPMC12623737 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature