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Predicting first-time-in-college students’ degree completion outcomes

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Abstract

About one-third of college students drop out before finishing their degree. The majority of those remaining will take longer than 4 years to complete their degree at “4-year” institutions. This problem emphasizes the need to identify students who may benefit from support to encourage timely graduation. Here we empirically develop machine learning algorithms, specifically Random Forest, to accurately predict if and when first-time-in-college undergraduates will graduate based on admissions, academic, and financial aid records two to six semesters after matriculation. Credit hours earned, college and high school grade point averages, estimated family (financial) contribution, and enrollment and grades in required gateway courses within a student’s major were all important predictors of graduation outcome. We predicted students’ graduation outcomes with an overall accuracy of 79%. Applying the machine learning algorithms to currently enrolled students allowed identification of those who could benefit from added support. Identified students included many who may be missed by established university protocols, such as students with high financial need who are making adequate but not strong degree progress.

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Acknowledgements

A.B., E.A., and M.D. were supported by NSF award 1742461 and M.D. by NSF award 1820862. We would like to thank our partners in the Office of Institutional Research (especially Derrick Isler) and the Financial Aid Office (Wally Anderson and Bruce Blackmon) for providing us with data and support for interpreting shared information. We thank our academic advisors for their expert guidance and feedback on our predictive models.

Funding

This research was supported by the UNC System Office’s Student Success Innovation Lab under Grant GR-000007639.

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Authors and Affiliations

  1. Office of Assessment & Accreditation, University of North Carolina at Charlotte, Fretwell 314E, 9201 University City Blvd., Charlotte, NC, 28223, USA

    Elise Demeter

  2. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, USA

    Mohsen Dorodchi, Erfan Al-Hossami & Aileen Benedict

  3. Office of Undergraduate Education, University of North Carolina at Charlotte, Charlotte, USA

    Lisa Slattery Walker & John Smail

  4. Department of Sociology, University of North Carolina at Charlotte, Charlotte, USA

    Lisa Slattery Walker

  5. Department of History, University of North Carolina at Charlotte, Charlotte, USA

    John Smail

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  1. Elise Demeter
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Contributions

All authors contributed to the study conception and design. Funding acquisition was obtained by Elise Demeter, Lisa Walker, and John Smail. Data collection and analysis were performed by Erfan Al-Hossami, Mohsen Dorodchi, and Elise Demeter. The first draft of the manuscript was written by Elise Demeter, Erfan Al-Hossami, and Aileen Benedict, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Elise Demeter.

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Demeter, E., Dorodchi, M., Al-Hossami, E. et al. Predicting first-time-in-college students’ degree completion outcomes. High Educ 84, 589–609 (2022). https://doi.org/10.1007/s10734-021-00790-9

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