Predicting time to graduation at a large enrollment American university
American student's often face different conditions that might increase or decrease the time it takes for them to get an undergraduate degree. These conditions include their background, the academic performance at university, and their integration into the social communities of the university they attend.
Understanding how these conditions impact students is important given the expense of a degree in America. This study presents data for 160,933 students attending a large American research university.
Using a machine learning technique called gradient boosting we present time-to-graduation predictive models trained on data that includes academic performance, enrollment, demographics, and features regarding a student's preparation for university.
We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).