Early Indicators of Student Success

This paper reports the results of a four-state collaboration––Texas, New York, Virginia, and Illinois––that uses Student Unit Record Database Systems that track students from high school into college. The goal is to determine whether it is possible to accurately predict whether individual students will not graduate using very early indicators available at college entry or during the first semester. Using similar statistical models across four state university systems, we identify individual students at greatest risk of non-completion quite accurately at early stages, allowing college staff to prioritize interventions and supports aimed at improving completion for those at greatest risk. Our logistic regression models rely on variables available to university administrators at student entry, including high school GPA, standardized test scores, parental income, remediation requirements, declared major, and college credits attempted in the first semester. Our models do not use gender, race, or ethnicity in determining probability of non-completion, making them useful for public university administrators. The fact that the same factors accurately predict graduation and non-completion in four very different state contexts suggests that similar dynamics are at play across the country. Our findings suggest that current commercial products that require extensive effort from faculty to input data on student progress, to act as an early warning system, may be unnecessary. More easily obtainable data can accurately predict students at risk of non-completion.