Trace the Source: Behavioral Drift Discrimination via Profile Replay for Knowledge Tracing

Knowledge Tracing (KT) aims to dynamically model students’ mastery of knowledge concepts from their historical learning interaction sequences. Most methods rely on item IDs and binary correctness. This makes it difficult to separate true ability changes from short-term drift, leading to biased mastery updates. To address this issue, we propose BddKT, a Behavioral Drift Discrimination via Profile Replay for Knowledge Tracing. At each interaction, BddKT uses the LLM to build an item profile and integrates it with sequential representations. This design provides multi-source evidence for separating drift from true ability change. Moreover, we design a temporal model with a profile-replay forgetting mechanism to obtain drift-robust current states. In addition, we further introduce a profile replay calibration module that retrieves relevant historical profiles and uses gated fusion to correct drift-driven updates. Extensive experiments on four public datasets show that BddKT outperforms SOTA KT models in predictive accuracy and drift discrimination. It achieves up to 6.99% absolute improvement in AUC and 7.42% absolute improvement in ACC. Our code and experimental log can be found in the supplementary material.