Research in Higher Education

, Volume 57, Issue 5, pp 544–569 | Cite as

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

Article

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 

References

  1. ACT. (2006). Developing the STEM education pipeline. Retrieved from http://www.act.org/research/policymakers/pdf/ACT_STEM_PolicyRpt.pdf.
  2. Adelman, C. (2005). Moving into townand moving on. The community college in the lives of traditional-age students. Washington, DC: U.S. Department of Education.Google Scholar
  3. Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.Google Scholar
  4. Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules in large databases (Research Report RJ 9839). San Jose, CA: IBM Almaden Research Center.Google Scholar
  5. American Association of Community Colleges. (2015). Community college trends and statistics. Retrieved from http://www.aacc.nche.edu/PUBLICATIONS/DATAPOINTS/Pages/default.aspx.
  6. Amey, M. J., Eddy, P. L., & Campbell, T. G. (2010). Crossing boundaries creating community college partnerships to promote educational transitions. Community College Review, 37(4), 333–347. doi:10.1177/0091552110365725.CrossRefGoogle Scholar
  7. Anderson, E., & Kim, D. (2006). Increasing the success of minority students in science and technology. Retrieved from the American Council on Education website http://www.acenet.edu/news-room/Documents/Increasing-the-Success-of-Minority-Students-in-Science-and-Technology-2006.pdf.
  8. Bach, S. K., Banks, M. T., Kinnick, M. K., Ricks, M. F., Stoering, J. M., & Walleri, R. D. (2000). Student attendance patterns and performance in an urban postsecondary environment. Research in Higher Education, 41(3), 315–330. doi:10.1023/A:1007038726940.CrossRefGoogle Scholar
  9. Bahr, P. R. (2013). The deconstructive approach to understanding community college students’ pathways and outcomes. Community College Review, 41(2), 137–153. doi:10.1177/0091552113486341.CrossRefGoogle Scholar
  10. Bailey, T., Jaggars, S. S., & Jenkins, D. (2015). Redesigning America’s community colleges: A clearer path to student success. Cambridge, MA: Harvard University Press.Google Scholar
  11. Bensimon, E. M., & Dowd, A. C. (2012). Developing the capacity of faculty to become institutional agents for Latinos in STEM. Retrieved from University of Southern California Center for Urban Education website http://cue.usc.edu/assests/Bensimon_Developing%20IAs_NSF%20Report%20CUE_2012.pdf.
  12. Bragg, D. D. (2012). Two-year college mathematics and student progression in STEM programs of study. In S. Olson & J. B. Labov (Eds.), Community colleges in the evolving STEM education landscape (pp. 80–101). Washington, DC: The National Academies Press.Google Scholar
  13. Burke, R., & Mattis, M. (2007). Women and minorities in science, technology, engineering and mathe- matics: Upping the numbers. Northampton, MA: Edward Elgar Publishing Inc.CrossRefGoogle Scholar
  14. Calcagno, J. C., Crosta, P. M., Bailey, T., & Jenkins, D. (2007). Stepping stones to a degree: The impact of enrollment pathways and milestones on community college student outcomes. Research in Higher Education, 48(7), 775–801. doi:10.1007/s11162-007-9053-8.CrossRefGoogle Scholar
  15. Chaplot, P., Rassen, E., Jenkins, D., & Johnstone, R. (2013). Principles of redesign: Promising approaches to transforming student outcomes. Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/principles-redesign-promising-approaches-cbd.pdf.
  16. Chen, X., & Weko, T. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education (NCES 2009-161). Retrieved from the National Center for Education Statistics website http://nces.ed.gov/pubs2009/2009161.pdf.
  17. Cohen, A. M., Brawer, F. B., & Kisker, C. B. (2014). The American community college (6th ed.). San Francisco, CA: Jossey-Bass.Google Scholar
  18. Cohen, A., & Ignash, J. (1994). An overview of the total college curriculum. New Directions for Community Colleges, 1994(86), 13–29. doi:10.1002/cc.36819948604.CrossRefGoogle Scholar
  19. Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution. American Educational Research Journal, 46(4), 924–942. doi:10.3102/0002831209349460.CrossRefGoogle Scholar
  20. Crosta, P. M. (2014). Intensity and attachment: How the chaotic enrollment patterns of community college students relate to educational outcomes. Community College Review, 42(2), 118–142. doi:10.1177/0091552113518233.CrossRefGoogle Scholar
  21. de Cohen, C. C., & Deterding, N. (2009). Widening the net: National estimates of gender disparities in engineering. Journal of Engineering Education, 98(3), 211–226. doi:10.1002/j.2168-9830.2009.tb01020.x.CrossRefGoogle Scholar
  22. Dowd, A. C. (2012). Developing supportive STEM community college to four-year college and university transfer ecosystems. In S. Olson & J. B. Labov (Eds.), Community colleges in the evolving STEM education landscape (pp. 107–134). Washington, DC: The National Academies Press.Google Scholar
  23. Espinosa, L. L. (2011). Pipelines to pathways: Women of color in undergraduate STEM majors and the college experiences that contribute to persistence. Harvard Educational Review, 81(2), 209–240.CrossRefGoogle Scholar
  24. Fox, M. A. (2003). Pan-organizational summit on the U.S. science and engineering workforce: Meeting summary. Retrieved from the National Center for Biotechnology Information website http://www.ncbi.nlm.nih.gov/books/NBK36359/pdf/TOC.pdf.
  25. Gabbard, G., Singleton, S., Macias, E. E., Dee, J., Bensimon, E. M., Dowd, A. C., et al. (2006). Practices supporting transfer of low-income community college transfer students to selective institutions: Case study findings. Boston, MA: University of Massachusetts Boston and University of Southern California.Google Scholar
  26. Hacker, A. (2015, July 9). The frenzy about high-tech talent. The New York Reviews of Books. Retrieved from http://www.nybooks.com/articles/archives/2015/jul/09/frenzy-about-high-tech-talent/.
  27. Hagedorn, L. S. (2005). Transcript analyses as a tool to understand community college student academic behaviors. Journal of Applied Research in the Community College, 13(1), 45–57.Google Scholar
  28. Hagedorn, L. S., Cabrera, A., & Prather, G. (2010). The community college transfer calculator: Identifying the course-taking patterns that predict transfer. Journal of College Student Retention, 12(1), 105–130. doi:10.2190/CS.12.1.g.CrossRefGoogle Scholar
  29. Hagedorn, L. S., & DuBray, D. (2010). Math and science success and nonsuccess: Journeys within the community college. Journal of Women and Minorities in Science and Engineering, 16(1), 31–50. doi:10.1615/JWomenMinorScienEng.v16.i1.30.CrossRefGoogle Scholar
  30. Hagedorn, L. S., & Kress, A. M. (2008). Using transcripts in analyses: Directions and opportunities. New Directions for Community Colleges, 2005(143), 7–17. doi:10.1002/cc.331.CrossRefGoogle Scholar
  31. Hagedorn, L. S., Maxwell, W. E., Cypers, S., Moon, H. S., & Lester, J. (2007). Course shopping in urban community colleges: An analysis of student drop and add activities. The Journal of Higher Education., 78(4), 464–485. doi:10.1353/jhe.2007.0023.CrossRefGoogle Scholar
  32. Hagedorn, L. S., & Purnamasari, A. V. (2012). A realistic look at STEM and the role of community colleges. Community College Review, 40(2), 145–164. doi:10.1177/0091552112443701.CrossRefGoogle Scholar
  33. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham, MA: Morgan Kaufmann.Google Scholar
  34. Haselgrove, S. (1994). Why the student experience matters. In S. Haselgrove (Ed.), the student experience (pp. 3–8). Buckingham: Society for Research into Higher Education and Open University Press.Google Scholar
  35. Hoffman, E., Starobin, S. S., Laanan, F. S., & Rivera, M. (2010). Role of community colleges in STEM education: Thoughts on implications for policy, practice, and future research. Journal of Women and Minorities in Science and Engineering, 16(1), 85–96. doi:10.1615/JWomenMinorScienEng.v16.i1.60.CrossRefGoogle Scholar
  36. Jackson, D. L., & Laanan, F. S. (2011). The role of community colleges in educating women in science and engineering. New Directions for Institutional Research, 2011(152), 39–49. doi:10.1002/ir.407.CrossRefGoogle Scholar
  37. Jankowski, N. A., & Marshall, D. W. (2014). Roadmap to enhanced student learning: Implementing the DQP and Tuning. Champaign, IL: National Institute for Learning Outcomes Assessment and Institute for Evidence-Based Change.Google Scholar
  38. Jaschik, S. (2015, April 13). UCLA faculty approves diversity requirement. Inside Higher Ed. Retrieved from https://www.insidehighered.com/news/2015/04/13/ucla-faculty-approves-diversity-requirement.
  39. Jenkins, D., & Cho, S. -W. (2012). Get with the program: Accelerating community college students’ entry into and completion of programs of study (CCRC Working Paper No. 32). Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/accelerating-student-entry-completion.pdf.
  40. Jenkins, D., & Cho, S.-W. (2014). Get with the program … and finish it: Building guided pathways to accelerate student completion. New Directions for Community Colleges, 2014(164), 27–35. doi:10.1002/cc.20078.Google Scholar
  41. Kopko, E., & Cho, S.-W. (2013). Timing of concentration, completion, and exit in community college (CCRC Analytics Report). Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/timing-of-concentration-completion-exit.pdf.
  42. Leinbach, D. T., & Jenkins, D. (2008). Using longitudinal data to increase community college student success: A guide to measuring milestone and momentum point attainment (CCRC Research Tools No. 2). Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/longitudinal-data-momentum-point-research-tool.pdf.
  43. Linde, N. (2011, July 13). Bring girls into the science-major pipeline, The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Bringing-Girls-Into-the/128099/.
  44. Lowell, B. L., & Salzman, H. (2007). Into the eye of the storm: Assessing the evidence on science and engineering education, quality, and workforce demand. Retrieved from The Urban Institute website http://www.urban.org/sites/default/files/alfresco/publication-pdfs/411562-Into-the-Eye-of-the-Storm.PDF.
  45. Luan, J., & Zhao, C. (2006). Practicing data mining for enrollment management and beyond. New Direction for Institutional Research, 2006(131), 117–122. doi:10.1002/ir.191.CrossRefGoogle Scholar
  46. National Science Board. (2015). Revisiting the STEM workforce: A companion to science and engineering indicators 2014. Retrieved from the National Science Foundation website http://www.nsf.gov/nsb/publications/2015/nsb201510.pdf.
  47. Packard, B. W.-L. (2012). Effective outreach, recruitment, and mentoring into STEM pathways: Strengthening partnerships with community colleges. In S. Olson & J. B. Labov (Eds.), Community colleges in the evolving STEM education landscape (pp. 58–79). Washington, DC: The National Academies Press.Google Scholar
  48. Palmer, R. T., & Wood, J. L. (2013). Community colleges and STEM: Examining underrepresented racial and ethnic minorities. New York, NY: Routledge.Google Scholar
  49. Porter, S. R., & Umbach, P. D. (2006). College major choice: An analysis of person-environment fit. Research in Higher Education, 47(4), 429–449. doi:10.1007/s11162-005-9002-3.CrossRefGoogle Scholar
  50. Richardson, J. T. E., & King, E. (1998). Adult students in higher education: Burden or boon? The Journal of Higher Education, 69(1), 65–88. doi:10.2307/2649182.CrossRefGoogle Scholar
  51. Riegle-Crumb, C., & King, B. (2010). Questioning a White male advantage in STEM: Examining disparities in college major by gender and race/ethnicity. Educational Researcher, 39(9), 656–664. doi:10.3102/0013189X10391657.CrossRefGoogle Scholar
  52. Roksa, J., & Calcagno, J. C. (2010). Catching up in community colleges: Academic preparation and transfer to four-year institutions. Teachers College Record, 112(1), 260–288.Google Scholar
  53. Roksa, J., & Keith, B. (2008). Credits, time, and attainment: Articulation policies and success after transfer. Educational Evaluation and Policy Analysis, 30(3), 236–254. doi:10.3102/0162373708321383.CrossRefGoogle Scholar
  54. Sax, L. J., Shapiro, C. A., & Eagan, M. K. (2011). Promoting mathematical and computer self-concept among female college students: Is there a role of single-sex secondary education? Journal of Women and Minorities in Science and Engineering, 17(4), 325–355. doi:10.1615/JWomenMinorScienEng.2011002386.CrossRefGoogle Scholar
  55. Scott-Clayton, J. (2011). The shapeless river: Does a lack of structure inhibit students’ progress at community college (CCRC Working Paper No. 25). Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/shapeless-river.pdf.
  56. Seymour, E. (1995). The loss of women from science, mathematics, and engineering undergraduate majors: An explanatory account. Science Education, 79(4), 437–473. doi:10.1002/sce.3730790406.CrossRefGoogle Scholar
  57. Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.Google Scholar
  58. Starobin, S. S., & Laanan, F. S. (2008). Broadening female participation in science, technology, engineering and mathematics: Experiences at community colleges. New Directions for Community Colleges, 2008(142), 37–46. doi:10.1002/cc.323.CrossRefGoogle Scholar
  59. Tyson, W., Lee, R., Borman, K. M., & Hanson, M. A. (2007). Science, technology, engineering, and mathematics (STEM) pathways: High school science and math coursework and postsecondary degree attainment. Journal of Education for Students Placed at Risk, 12(3), 243–270. doi:10.1080/10824660701601266.CrossRefGoogle Scholar
  60. Wang, X. (2013a). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121. doi:10.3102/0002831213488622.CrossRefGoogle Scholar
  61. Wang, X. (2013b). Modeling entrance into STEM fields of study among students beginning at community colleges and four-year institutions. Research in Higher Education, 54(6), 664–692. doi:10.1007/s11162-013-9291-x.CrossRefGoogle Scholar
  62. Wang, X. (2015). Pathway to a baccalaureate in STEM fields: Are community colleges a viable route and does early STEM momentum matter? Educational Evaluation and Policy Analysis, 37(3), 376–393. doi:10.3102/0162373714552561.CrossRefGoogle Scholar
  63. Wao, H. O., Lee, R. S., & Borman, K. (2010). Climate for retention to graduation: A mixed methods investigation of student perceptions of engineering departments and programs. Journal of Women and Minorities in Science and Engineering, 16(4), 293–318. doi:10.1615/JWomenMinorScienEng.v16.i4.20.CrossRefGoogle Scholar
  64. Zeidenberg, M., & Scott, M. (2011). The context of their coursework: Understanding course-taking patterns at community colleges by clustering student transcripts (CCRC Working Paper No. 35). Retrieved from Columbia University, Community College Research Center website http://ccrc.tc.columbia.edu/media/k2/attachments/coursework-patterns-clustering-transcript.pdf.

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

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

Personalised recommendations