Graduation rates of four-year institutions are an increasingly important metric to incoming students and for ranking universities. To increase completion rates, universities must analyze available student data to understand trends and factors leading to graduation. Using predictive modeling, incoming students can be assessed as to their likelihood of completing a degree. If students are predicted to be most likely to drop out, interventions can be enacted to increase retention and completion rates.
At the University of Nevada, Las Vegas (UNLV), four-year graduation rates are 15% and six-year graduation rates are 39%. To improve these rates, we have gathered seven years worth of data on UNLV students who began in the fall 2010 semester or later up to the summer of 2017 which includes information from admissions applications, financial aid, and first year academic performance. The student group which is reported federally are first-time, full-time freshmen beginning in the summer or fall. Our data set includes all freshmen and transfer students within the time frame who meet our criteria. We applied data analysis and visualization techniques to understand and interpret this data set of 16,074 student profiles for actionable results by higher education staff and faculty. Predictive modeling such as logistic regression, decision trees, support vector machines, and neural networks are applied to predict whether a student will graduate. In this analysis, decision trees give the best performance
