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cs.LG2026
RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer
Samuel Girard, Juan D. Pinto, Jill-Jênn Vie +1
As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced interpretability, which is cru…
cs.LG2025
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors
Juan D. Pinto, Luc Paquette
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability…
cs.LG2024
Towards a Unified Framework for Evaluating Explanations
Juan D. Pinto, Luc Paquette
The challenge of creating interpretable models has been taken up by two main research communities: ML researchers primarily focused on lower-level explainability methods that suit…