An Explainable Machine Learning Approach to Predicting and Understanding Dropouts in MOOCs

Purpose: The purpose of this study is to predict dropouts in two runs of the same MOOC using an explainable machine learning approach. With the explainable approach, we aim to enable the interpretation of the black-box predictive models from a pedagogical perspective and to produce actionable insights for related educational interventions. The similarity and the differences in feature importance between the predictive models were also examined. Design/Methodology/Approach: This is a quantitative study performed on a large public dataset containing activity logs in a MOOC. In total, 21 features were generated and standardized before the analysis. Multi-layer perceptron neural network was used as the black-box machine learning algorithm to build the predictive models. The model performances were evaluated using the accuracy and AUC metrics. SHAP was used to obtain explainable results about the effects of different features on students’ success or failure. Findings: According to the results, the predictive models were quite accurate, showing the capacity of the features generated in capturing student engagement. With the SHAP approach, reasons for dropouts for the whole class, as we