3 papers
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…
cs.CY2024
Deep Learning for Educational Data Science
Juan D. Pinto, Luc Paquette
With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of…