Learning How Students Update: Graph-Conditioned Meta-Optimization of Cognitive State for Knowledge Tracing

Knowledge tracing predicts a learner's next response from a latent knowledge state and is a core component of adaptive tutoring systems. Recent models increasingly focus on richer representations of interaction histories, but the mechanism for updating the learner state is still typically fixed by the model architecture. On concept-level benchmarks, however, more expressive encoders do not consistently outperform simple recurrent models. We revisit this design choice and argue that a learner's response provides directional evidence about mastery, so the cognitive state should evolve through a learned, preconditioned gradient step on the learner's own prediction error. GRACE represents the cognitive state in concept coordinates, differentiates the prediction loss with respect to the state, and uses a graph-conditioned meta-optimizer to transform the resulting gradient into the next state. The meta-optimizer controls per-concept retention, adaptive step size, graph-based gradient diffusion, and diagonal-plus-low-rank preconditioning from the interaction context. Experiments on ASSIST2009, Junyi, and the AAAI2023 challenge data show that GRACE achieves the strongest overall predictive performance against thirteen baselines spanning recurrent, graph-based, and attention-based knowledge tracing models. Ablation studies identify the learned update mechanism as a major source of the performance gains, while sensitivity analyses consistently favor narrow embeddings.