5 citations · 5 across the 3 of their papers we have counts for
4 papers
A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models
Mélina Verger, Chunyang Fan, Sébastien Lallé +2
Predictive student models are increasingly used in learning environments. However, due to the rising social impact of their usage, it is now all the more important for these models…
Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
Valdemar Švábenský, Mélina Verger, Maria Mercedes T. Rodrigo +5
Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited w…
Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models
Mélina Verger, Sébastien Lallé, François Bouchet +1
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions.…
Predicting students' performance in online courses using multiple data sources
Mélina Verger, Hugo Jair Escalante
Data-driven decision making is serving and transforming education. We approached the problem of predicting students' performance by using multiple data sources which came from onli…