Predicting four-year student success from two-year student data

Denise Nadasen, Alexandra List

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

This chapter describes a study that evaluated the academic pathway of transfer students from two community colleges to a 4-year university. The project focused on a series of academic milestones that students must achieve prior to earning a 4-year credential. Those milestones include the first-term GPA, re-enrollment, and program completion. The purpose of this project was to develop an integrated database that contains key data on student demographics, course-taking behaviors, and performance from both the community college and the 4-year institution and to analyze the data using data mining and traditional statistical techniques to predict student success. A series of logistic regression equations identified significant predictors of first-term GPA, re-enrollment, and graduation. For example, overall rate of successful course completion, rate of successful math completion, rate of successful English completion, completion of developmental math were found to be significant predictors of a successful first-term GPA.

Original languageEnglish (US)
Title of host publicationBig Data and Learning Analytics in Higher Education
Subtitle of host publicationCurrent Theory and Practice
PublisherSpringer International Publishing
Pages221-236
Number of pages16
ISBN (Electronic)9783319065205
ISBN (Print)9783319065199
DOIs
StatePublished - Jan 1 2016

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All Science Journal Classification (ASJC) codes

  • Social Sciences(all)

Cite this

Nadasen, D., & List, A. (2016). Predicting four-year student success from two-year student data. In Big Data and Learning Analytics in Higher Education: Current Theory and Practice (pp. 221-236). Springer International Publishing. https://doi.org/10.1007/978-3-319-06520-5_13