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This paper introduces SLC (State-space Logit Correction), which corrects per-item logit bias in knowledge tracing models using empirical-Bayes shrinkage via a Kalman smoother, improving AUC beyond global calibration techniques.
This paper introduces MBP-KT, a framework for enhanced knowledge tracing that leverages meta-behavioral patterns to extract global collaborative information from learner interactions, improving performance across various downstream models.