Interpretable Student Performance Prediction Using Explainable Boosting Machine for Multi-Class Classification

Students’ performance prediction can have many uses in the education sector. It helps to take measures to support struggling students and to improve course delivery. However, having meaningful explanations along each prediction is essential for the reliability of the predictions and hence is desirable. In this work, we propose a method for predicting student performance while generating explanations of the predictions made. An Explainable Boosting Machine is implemented to suit multi-class classification to achieve the mentioned objective. The classification performance of the proposed approach is compared with similar supervised learning models, namely a linear model, a decision tree, and a decision rule-based approach for accuracy, precision, recall, and F1 Score. Results show that the Explainable Boosting Machine ranks second in classification performance. At the same time, it provides global and local explanations of the predictions, which are further shown to be consistent with the observations made in feature selection. The proposed approach and its extensions can help in predicting student performance while enabling the interpretation of the predictions made. It will enable educators to devise strategies to improve students’ performance.