Prediction of Graduation Delay Based on Student Performance

Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines (DBMs). We train those models using actual student data. In particular, we used high school GPA, ACT score, gender and ethnicity as the main feature set for training those models. The results show that the DBMs edges out SVMs and GPs in some regards, which has been discussed in detail in the paper, although the difference in performance among the models is negligible with respect to overall accuracies obtained.