Towards Explainability in Modern Educational Data Mining: A Survey

Basile Tousside, Yashwanth Dama, Jörg Frochte

Abstract: Data mining has become an integral part of many educational systems, where it provides the ability to explore hidden relationship in educational data as well as predict students’ academic achievements. However, the proposed techniques to achieve these goals, referred to as educational data mining (EDM) techniques, are mostly not explainable. This means that the system is black-boxed and offers no insight regarding the understanding of its decision making process. In this paper, we propose to delve into explainability in the EDM landscape. We analyze the current state-of-the-art method in EDM, empirically scrutinize their strengths and weaknesses regarding explainability and making suggestions on how to make them more explainable and more trustworthy. Furthermore, we propose metrics able to efficiently evaluate explainable systems integrated in EDM approaches, therefore quantifying the degree of explanability and trustworthiness of these approaches.