HDTR-KT: Mutually Regulated Knowledge Tracing with Hierarchical Local Evolution and Dual-Timescale Global Cognition

Knowledge tracing aims to infer students’ evolving knowledge states from historical question–response interactions to predict future performance. Despite the progress of recent deep models, existing approaches often lack an explicit mechanism for determining how different types of learning evidence should be transferred, retained, and coordinated across cognitive states with different abstraction levels and temporal characteristics. To address this limitation, this study proposes HDTR-KT, a knowledge tracing framework that models cognitive evolution through hierarchical evidence transition and dual-timescale state regulation. At the local level, HDTR-KT establishes an asymmetric transition from question-level experience to concept-level mastery, where question-specific increments are selectively transformed into concept messages rather than directly updating both states with identical interaction evidence. A constrained forgetting mechanism further preserves the hierarchical retention relationship between item memory and concept mastery. At the global level, different evidence sources are assigned to states with distinct temporal dynamics: prediction residuals drive an adaptive short-term state to capture transient deviations, whereas concept-level evidence is conservatively accumulated into a long-term state through a bounded update process. The fused global context subsequently regulates local-state forgetting, forming a closed-loop evolution mechanism between global cognition and localized memory. Extensive experiments on three real-world educational datasets demonstrate that HDTR-KT achieves consistently competitive predictive performance compared with representative baselines.