Modeling Success: Using Pre-enrollment Data to Identify Academically At-Risk Students

Improving student success and degree completion is one of the core principles of strategic enrollment management. To address this principle, institutional data were used to develop a statistical model to identify academically at-risk students. The model employs multiple linear regression techniques to predict students at risk of earning below a 2.0 grade point average (GPA) in their first semester of college. Data analysis from student cohorts starting in the Fall 2007 through Fall 2009 (N = 11,644) identified two groups of students—one predicted to earn less than a 2.0 and the other predicted to earn a 2.0 or higher. The first semester college GPA and retention rates of both groups of students were tracked to examine the accuracy of the model in predicting student success and subsequent retention rates. Multi-year analyses illustrates that the model can be used to identify students who are at risk of earning less than a 2.0 GPA. Additional analysis demonstrates there is a relationship between predicted and actual first semester GPA and retention rates. Since the data used to develop the model are commonly available at most institutions, this study provides a practical approach for the SEM research professional to identify potentially academically at-risk students, which subsequently can be used to assist students and improve student success and degree completion.

Ekaterina (Kate) Ralston is the Assistant Director for Research in Admissions at Iowa State University. Kate provides analytic support to the Office of Admissions as well as contributing to the projects conducted by the Enrollment Research Team. Kate has a doctoral degree in sociology, a master’s in mass communications from Iowa State University, and a bachelor’s in newspaper journalism from Moscow State University, Russia. Her areas of interest include multi-method approaches to data, focusing on quantitative techniques, such as structural equation modeling, longitudinal studies, and interaction analysis.

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