Research in Higher Education

, Volume 57, Issue 5, pp 544–569

Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways

Article

DOI: 10.1007/s11162-015-9397-4

Cite this article as:
Wang, X. Res High Educ (2016) 57: 544. doi:10.1007/s11162-015-9397-4

Abstract

This research focuses on course-taking patterns of beginning community college students enrolled in one or more non-remedial science, technology, engineering, and mathematics (STEM) courses during their first year of college, and how these patterns are mapped against upward transfer in STEM fields of study. Drawing upon postsecondary transcript data, collected as part of the Beginning Postsecondary Students Longitudinal Study (BPS:04/09), this study takes advantage of data mining techniques that, although underutilized in higher education research, are powerful and appropriate analytical tools for investigating complex transcript data. Thus, focusing on a pivotal yet extremely understudied topic dealing with postsecondary STEM education and pathways, this study offers new insight into course and program features that contribute to efficient and effective academic STEM pathways for community college students.

Keywords

Upward transfer STEM Course-taking Transcript analysis Data mining 

Funding information

Funder NameGrant NumberFunding Note
Association for Institutional Research
  • RG13-39

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Educational Leadership and Policy AnalysisUniversity of Wisconsin-MadisonMadisonUSA

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