DualKT: Dual-Timescale Knowledge Tracing via Orthogonal Decomposition and Target-Conditioned Fusion

Knowledge tracing (KT) predicts future performance from students' interaction histories. Accurate prediction requires modeling both long-term evolution across the full history and local fluctuations driven by recent interactions. Yet existing methods often encode both scales jointly or directly combine multiple encoders without explicitly modeling the geometric relationship between their representations. We propose DualKT, a dual-timescale model with orthogonal decomposition and target-conditioned fusion. DualKT constructs a structured question representation, encodes the full history into a long-term state with Mamba-2, and uses a target-querying hierarchical Transformer to extract a target-relevant short-term state from recent interactions and chunked full-prefix summaries. It decomposes the short-term state into a long-term-aligned component and an orthogonal residual, then fuses them with the long-term state conditioned on the target question. In five-fold question-level experiments on five public datasets, DualKT ranks among the top two methods in AUC on every dataset and first on four under fixed configurations. Ablations identify the structured question representation as the main source of improvement, with further gains from the dual-timescale branches and learned fusion. These results support explicit modeling and integration of long- and short-term cognitive information for target-specific prediction.