Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways
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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.
KeywordsUpward transfer STEM Course-taking Transcript analysis Data mining
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