In the 21st century, more students enroll in higher education and take federal loans to defer the cost of attendance resulting in average levels of borrowing steadily increasing. In the same timeframe, there is an increase in the number of students entering repayment for their federal loans and an increase in the proportion of individuals defaulting on their repayments. Individual institutions are responsible for the students who borrow money to attend their institution. As such, individual campuses are interested in knowing which specific factors affect their students' ability to repay their loans. Therefore, via a quantitative case study, I sought to understand if local, institution-specific data could improve the timing of the estimates of default probabilities at a nonresidential campus. My case study allowed me to examine how institutional-level data could inform institutional decision-making by showing which pre-college student characteristics, college experience by semester enrolled variables, and post-attendance factors associated with default for my subject institution. Specifically, by incorporating these institutional-level variables in an organized way (i.e., by type of activity or by semester), I could determine how early in a student's career I could identify them as "at-risk" of default. My findings show that institution-specific, student-level data at my subject institution provides a template to predict default sooner than the traditional default measures and provides a framework for other institutions to apply to their student populations. My results suggest existing default research may be missing an important set of variables in the models, variables measuring students' college experiences.
Read
