Predicting students' performance in online courses using multiple data sources. - Mélina Verger; Hugo Jair Escalante (CoRR, 2021)

Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models. - Mélina Verger; Sébastien Lallé; François Bouchet; Vanda Luengo (EDM, 2023)

A Fair Post-processing Method Based on the MADD Metric for Predictive Student Models. - Mélina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo (PKDD/ECML Workshops (2), 2023)

Fairness of MOOC Completion Predictions Across Demographics and Contextual Variables. - Sébastien Lallé; François Bouchet; Mélina Verger; Vanda Luengo (AIED (1), 2024)

Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students. - Valdemar Svábenský; Mélina Verger; Maria Mercedes T. Rodrigo; Clarence James G. Monterozo; Ryan S. Baker 001; Miguel Zenon Nicanor Lerias Saavedra; Sébastien Lallé; Atsushi Shimada 001 (EDM, 2024)

Algorithmic fairness analyses of supervised machine learning in education. (Analyses de l'équité algorithmique pour l'apprentissage automatique en éducation). - Mélina Verger (2024)