Using Machine Learning to Predict Student Success in Undergraduate Engineering Programs

Undergraduate engineering programs are typically considered some of the most challenging as their curricula require students to have an aptitude for math, science, and engineering. The resources (time, effort, funds) required to finish an engineering degree is substantial. Therefore, it is imperative that the engineering students are supported with well-informed academic guidance as early in their education as possible so that these resources can be used most effectively. Analytical and data-driven methods such as machine learning techniques can be used to inform this guidance process by predicting student success based on features such as individual traits and academic performance. In that direction, we investigated the effectiveness of using machine learning in predicting engineering student success based on academic performance in core math, physics, and engineering courses in three undergraduate engineering programs. The data categories selected for training and testing of the machine learning models in this study are common to most engineering programs nationwide and can be customized in a straightforward manner for other engineering disciplines. The methodology and results outlined in this preliminary study shows promise for predicting degree and cumulative GPA in our three engineering programs.