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

dc.contributor.author Gansemer-Topf, Ann
dc.contributor.author Compton, Jonathan
dc.contributor.author Gansemer-Topf, Ann
dc.contributor.author Wohlgemuth, Darin
dc.contributor.author Forbes, Gregory
dc.contributor.author Ralston, Katerina
dc.contributor.department School of Education
dc.date 2018-02-17T10:52:38.000
dc.date.accessioned 2020-06-30T02:15:49Z
dc.date.available 2020-06-30T02:15:49Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2015
dc.date.issued 2015-07-06
dc.description.abstract <p>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.</p>
dc.description.comments <p>This is the peer reviewed version of the following article: FULL CITE, which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/edu_pubs/37/
dc.identifier.articleid 1036
dc.identifier.contextkey 8029704
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath edu_pubs/37
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/22888
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/edu_pubs/37/2015_Gansemer_TopfAM_ModelingSuccess.pdf|||Fri Jan 14 23:49:23 UTC 2022
dc.source.uri 10.1002/sem3.20064
dc.subject.disciplines Education
dc.subject.disciplines Educational Assessment, Evaluation, and Research
dc.subject.disciplines Higher Education
dc.subject.disciplines Student Counseling and Personnel Services
dc.title Modeling Success: Using Preenrollment Data to Identify Academically At-Risk Students
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 214cc672-840d-4f5e-b925-3029d6d27c4f
relation.isOrgUnitOfPublication 385cf52e-6bde-4882-ae38-cd86c9b11fce
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