An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing

Knowledge tracing (KT) models are predominantly evaluated using aggregate metrics such as area under the curve (AUC) and accuracy. However, these global scores obscure where the remaining errors originate and fail to indicate whether a benchmark is approaching saturation.