Back to Basics: An Interpretable Multi-Class Grade Prediction Framework

Next-term grade prediction is a challenging problem. The objective of this problem is to predict students grades in new courses, given their grades in courses they have previously taken. Adopting various machine learning algorithms is a very common and straightforward approach to tackling this problem. However, such models are very difficult to interpret. That is, it is difficult to explain to a student (or a teacher) why the model predicted grade B for a given student for example. In this work, we shed light on the importance of building interpretable models for educational data mining tasks. Specifically, we propose a novel interpretable framework for multi-class grade prediction that is based on an optimal rule-list mining algorithm. Additionally, we evaluate our proposed framework on two private datasets and compare our results with baseline models. Our findings show that our proposed framework is capable of achieving higher prediction and interpretability values when compared to black-box models.