Modeling the Impact of Enrollment Patterns on Degree Completion for Community College Students
*Names in bold indicate Presenter
For community college students at the City University of New York (CUNY), data on student persistence and degree completion indicate that very few are able to earn an Associate degree in two years (3-4 percent), and many students take longer than three years. Many students will experience at least one period of non-enrollment (or, stopping-out) while they make progress toward a degree. Although existing research has shown that stopping-out of college decreases the likelihood of graduating, and has explored the factors affecting a student’s likelihood of stopping-out, relatively little is known about the effect that the timing of stopping-out has on a student’s probability of graduating.
This paper explores the extent to which various enrollment patterns (specifically, the degree to which students stop-out of college) impact the probability that students ultimately graduate or transfer to a four-year institution. Longitudinal enrollment data were obtained for a pooled cohort of students who enrolled as degree-seeking first-time freshmen from 2006 through 2008 at CUNY community colleges. The total sample included over 30,000 students attending six different two-year institutions. Student records were assessed over a period of twelve semesters (six years) beginning with their term of initial enrollment. Using a discrete-time hazard model – a multivariate regression method seldom employed in educational research – this study was able to model changes in probabilities of graduation or transfer over time (Allison, 1984). Furthermore, the model was able to incorporate the effects of important covariates, some of which remained constant throughout the duration of the analysis (such as race/ethnicity) and others that changed over time (such as academic credits attempted). This allowed for the effects of enrollment patterns to be calculated while controlling for other explanatory variables, as well as the analysis of interaction effects between enrollment patterns and key demographic traits.
As public policy initiatives continue to recognize the importance of community colleges in providing educational access to groups of students who have traditionally been underrepresented in higher education, a better understanding of these students – specifically as it pertains to enrollment trends affecting degree completion – is needed in order to improve student outcomes. This paper builds on existing knowledge about community college students, advancing a model of student enrollment patterns that may inform the direction of future policy in higher education.