ReTraceKT : Relevance-guided Multimodel State-space Knowledge Tracing

Knowledge tracking (KT) estimates a learner's evolving knowledge state from a sequence of exercise responses. Two properties of modern learning platforms make this problem harder: essential evidence is often embedded in diagrams rather than text, and long histories contain many interactions that are irrelevant to the next exercise. We present ReTraceKT, a multimodal KT framework that retrieves semantically relevant evidence before tracking knowledge dynamics. ReTraceKT first uses a vision-language model offline to render each exercise image into structured descriptions and aligns them with trainable exercise embeddings. It then builds a threshold semantic graph over exercises and uses the graph to retrieve a sparse, question-conditioned summary of a learner's history. Finally, a selective state-space tracer combines local response patterns with long-range state transitions in linear sequence complexity. Experiments on NIPS34, MOOC-Radar, and XES3G5M show consistent improvements over strong recurrent, attention-based, and sparse KT baselines. Relative to the strongest baseline, ReTraceKT improves AUC by 3.22%, 1.82%, and 2.08% on the three datasets, respectively; ablations confirm that semantic rendering and relevance retrieval are complementary. Overall, ReTraceKT delivers consistently strong and stable performance across the three datasets.