Stable and Faithful Explanations for Knowledge Tracing

The paper introduces a validation protocol for knowledge‑tracing models that jointly assesses predictive performance, explanation stability, and faithfulness. Using engineered behavioral features from ASSISTments data, the authors compare an XGBoost model explained with TreeSHAP against four deep‑learning baselines, finding comparable predictive accuracy when information is matched and demonstrating that TreeSHAP rankings are stable and impactful. The study highlights how data preprocessing (e.g., rebuilding the 2009 dataset) can affect both model performance and explanation outcomes.

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