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Stable and Faithful Explanations for Knowledge Tracing
Not provided in the abstract
knowledge tracingexplainabilitymachine learningeducational technology
2609.28502
Builder Relevance
1h ago70%
Abstract
This study presents a validation protocol for knowledge tracing models that assesses predictive competitiveness, explanation stability, and retraining-based faithfulness.
Reality Card
Core Claim
The study demonstrates that an Extreme Gradient Boosting model can achieve competitive predictive performance and stable explanations compared to deep learning baselines in knowledge tracing.
Method / Result
XGBoost achieved an area under the curve (AUC) of 0.786 on the rebuilt 2009 dataset.
Limitations
The study's reliance on engineered behavioral features from specific datasets may limit generalizability and reproducibility.
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