  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 to be both sufficiently accurate and fair in their
predictions. To evaluate algorithmic fairness, a new metric has been developed
in education, namely the Model Absolute Density Distance (MADD). This metric
enables us to measure how different a predictive model behaves regarding two
groups of students, in order to quantify its algorithmic unfairness. In this
paper, we thus develop a post-processing method based on this metric, that aims
at improving the fairness while preserving the accuracy of relevant predictive
models' results. We experiment with our approach on the task of predicting
student success in an online course, using both simulated and real-world
educational data, and obtain successful results. Our source code and data are
in open access at https://github.com/melinaverger/MADD .
