Early Student Grade Prediction: An Empirical Study

Abstract

In higher educational institutes, early grade prediction is an important area of interest as it allows instructors to improve students’ performance in their courses by providing special attention at the early stages. Machine learning techniques can be utilized for students’ grades prediction in different courses. However, the performance of these techniques is highly dependent on the quality of data that made the selection of model a challenging task. Therefore, in this paper, we evaluate different state-of-the-art machine learning techniques for university students grade prediction. Ultimately we find that Restricted Boltzmann Machines (RBM) can more accurately predict students’ grades. The predicted grades by these techniques visualize uncertainty on student learning and can be used for confidence gains, student degree planning, personalized advising, and to enable instructors to identify potential students who might need assistance in relevant courses.