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

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.

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Fig. 1

Notes

  1. I also considered including attempted but failed courses in the analyses. After some exploration, I found that this approach did not add much to revealing meaningful results that point to successful STEM transfer paths, as failed attempts would mostly appear in the non-transfer path. Therefore, given the focus of the study on successful course-taking patterns leading to different STEM transfer outcomes, I retained successfully completed coursework only to help maintain the study focus and better interpretability of the findings.

  2. For a detailed description of STEM course classification, see the section on data preparation and formulation.

  3. For those students who transferred, the transfer cut-off date is based on students’ first transfer to a 4-year institution. For students who did not transfer, course-taking records over the 6-year observation window were retained.

  4. I included remedial courses when categorizing coursework in order to account for the full range of course-taking among the study sample. After applying the sampling criteria, the proportion of the sample members with passed remedial courses is relatively small (roughly 1 %). While it would be interesting to explore STEM pathways for students with different levels of remediation, this approach is not feasible given the subsample sizes in this study, and should be pursued in future research.

  5. I should note that one limitation with the frequent patterns discovered by the Decision List algorithm is the relatively small number of antecedent items as compared with the ones discovered by the Apriori algorithm, due to the fact that it is much more challenging to find the same or similar course-credit patterns than to identify whether students took the same courses or not. This limitation is more present as the course-credit patterns get more erratic, as indicated in the non-transfer outcome category (bottom third of Table 7). However, I would argue that, given the study’s focus on revealing successful transfer course pathways, this limitation does not affect the study findings in major ways.

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Acknowledgments

This study is based upon work supported by the Association for Institutional Research, the National Science Foundation, the National Center for Education Statistics, and the National Postsecondary Education Cooperative under Association for Institutional Research Grant Number RG13-39. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Association for Institutional Research, the National Science Foundation, the National Center for Education Statistics, and the National Postsecondary Education Cooperative.

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Wang, X. Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways. Res High Educ 57, 544–569 (2016). https://doi.org/10.1007/s11162-015-9397-4

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Keywords

  • Upward transfer
  • STEM
  • Course-taking
  • Transcript analysis
  • Data mining