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 .

翻译：预测学生模型在学习环境中正得到日益广泛的应用。然而，鉴于其使用带来的社会影响日益凸显，确保这些模型在保持足够预测准确性的同时具备公平性显得尤为重要。为评估算法公平性，教育领域提出了一种新度量标准——模型绝对密度距离（MADD）。该度量能够量化预测模型针对不同学生群体的行为差异，从而衡量算法不公平程度。本文基于该度量开发了一种后处理方法，旨在提升预测模型结果的公平性同时保持其准确性。我们通过在线课程学生成绩预测任务验证该方法，使用模拟与真实教育数据进行实验，并取得了良好效果。相关源代码与数据已在https://github.com/melinaverger/MADD公开。