Accurately assessing students’ knowledge states and dynamically adapting instructional interactions to their cognitive levels are fundamental to optimizing personalized learning. However, conventional knowledge tracing (KT) approaches are constrained by three critical limitations: data sparsity undermines prediction robustness, the neglect of forgetting behavior misrepresents real learning processes, and static knowledge-state modeling fails to capture learners’ dynamic cognitive changes. To overcome these shortcomings, this study proposes DRAKT (Dynamic Reinforcement learning-based Adaptive Knowledge Tracing), a novel model that introduces two key innovations: (1) a Q-learning-based knowledge-state adjustment mechanism, which dynamically updates mastery levels via a reward structure integrated with the Ebbinghaus forgetting curve; and (2) a dynamic memory update module that combines a gated recurrent unit (GRU) with attention-based filtering to capture long-term learning dependencies and suppress irrelevant memory traces. Experiments conducted on three public ASSISTments datasets (2009, 2012, and 2017) demonstrate that DRAKT consistently outperforms state-of-the-art baselines. On ASSISTments2017 and ASSISTments2009, DRAKT achieves AUC scores of 82.08% and 81.47%, respectively, surpassing the second-best model (GKT) by 2.75–6.57 percentage points in AUC and 4.77–5.75 percentage points in accuracy. In practice, DRAKT offers a reliable technical foundation for enabling personalized learning-path recommendation and real-time cognitive adaptation in intelligent educational systems.
Li et al. (Tue,) studied this question.