The middle-man between models and mentors: SHAP values to explain dropout prediction models in higher education

Abstract

One of the challenges of prediction or classification models in education is that the best performing models usually come in a "black box", meaning that it is almost impossible for non-data scientists (and sometimes even experienced researchers) to understand the rationale behind a model prediction. In this poster we show how SHAP (SHapley Additive exPlanations) values can be used for model explainability as a baseline, and how this same tool might be used for further variable analysis and possibly even bias detection by obtaining SHAP values and figures for two dropout prediction models trained with student data from two different educational models implemented in the same University.