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Regional Educational Laboratory Program

Who will succeed and who will struggle? Predicting early college success with Indiana’s Student Information System

This study examined whether data on Indiana high school students, their high schools, and the Indiana public colleges and universities in which they enroll predict their academic success during the first two years in college. The researchers obtained student-level, school-level, and university-related data from Indiana’s state longitudinal data system on the 68,802 students who graduated high school in 2010. For the 32,564 graduates who first entered a public 2-year or 4-year college, the researchers examined their success during the first two years of college using four indicators of success: (1) enrolling in only nonremedial courses, (2) completion of all attempted credits, (3) persistence to the second year of college, and (4) an aggregation of the other three indicators. HLM was used to predict students’ performance on indicators using students’ high school data, information about their high schools and information about the colleges they first attended. Half of Indiana 2010 high school graduates who enrolled in a public Indiana college were successful by all indicators of success. College success differed by student demographic and academic characteristics, by the type of college a student first entered, and by the indicator of college success used. Academic preparation in high school predicted all indicators of college success, and student absences in high school predicted two individual indicators of college success and a composite of college success indicators. While statistical relationships were found, the predictors collectively only predicted less than 35 percent of the variance. The predictors from this study can be used to identify students who will likely struggle in college, but there will likely be false positive (and false negative) identifications. Additional research is needed to identify other predictors—possibly non-cognitive predictors—that can improve the accuracy of the identification models.
Publication Type:
Making Connections
Online Availability:
Publication Date:
March 2015