Improving course success prediction using ABET course outcomes and grades

Abstract

Modeling and prediction of student success is a critical task in education. In this paper, we employ machine learning methods to predict course grade performance of Computer Engineering students. As features, in addition to the conventional course grades we use fine grained student performance measurements corresponding to different goals (ABET outcomes) of a course. We observe that, compared to using only previous course grades, addition of outcome grades can significantly improve the prediction results. Using the trained model enables interpretation of how different courses affect performance on a specific course in the future. We think that even more detailed and systematically produced course outcome measurements can be beneficial in modeling students university performance.