Dual Behavior Sequence Enhancement and Conditional Diffusion for Cognitive Diagnosis

Cognitive diagnosis aims to infer students' latent cognitive states from their historical learning behaviors and predict their knowledge mastery on target exercises.Although existing methods have achieved promising results in cognitive state modeling, they still inadequately exploit temporal dynamic behaviors during students' learning processes. Moreover, ability representations derived from limited historical interaction statistics are susceptible to response fluctuations.To address these limitations, this paper proposes a Dual Behavior Sequence Enhancement and Conditional Diffusion method for Cognitive Diagnosis (DBSCD).The proposed method centers on modeling students’ response sequences and time-interval sequences from historical interactions, while incorporating corresponding exercise and knowledge concept information to construct a more comprehensive representation of students’ historical cognitive states.This enhanced state is then employed as a condition to guide the conditional diffusion process in progressively refining explicit ability representations, thereby yielding more robust student cognitive states.Finally, the model integrates target exercise-related information to perform fine-grained cognitive diagnosis at the knowledge concept level.Experimental results on two real-world educational datasets demonstrate that DBSCD achieves favorable diagnostic performance, validating the effectiveness of both dual behavior sequence modeling and the conditional diffusion-based ability refinement mechanism.The source code is available at https://anonymous.4open.science/r/DBSCD