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Exploring the Relationship Between Community College Students’ Exposure to Math Contextualization and Educational Outcomes

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Abstract

Low completion rates of math courses pose a major challenge for community college students’ educational progress and outcomes. Contextualized instruction has been identified as a promising approach to removing some of the longstanding barriers within math teaching and learning. Still, the empirical base on this topic remains small, with particularly little evidence on how exposure to math contextualization relates to students’ longer-term educational outcomes, which bear important implications for institutions’ performance metrics and policymaking. We contribute new research on this front by examining how exposure to math contextualization relates to a range of interim and longer-term educational outcomes at a large community college in a Midwestern state. We applied the genetic matching approach to construct a study sample that was balanced in background characteristics between the student group receiving contextualized math instruction and their counterparts enrolled in traditional math courses. We adopted a set of regression analyses based on the matched sample, and found a significant positive relationship between exposure to math contextualization and students’ outcome measures, including course performance, term GPA, continuous postsecondary enrollment, credential completion, and upward transfer.

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Notes

  1. We acknowledge that, in reality, very few social activities such as teaching and learning in the classroom can be described as fully decontextualized. Thus, we adopt the term from the literature but put it in quotation marks. In these “decontextualized” classes, examples from real life may well have occurred, but contextualization goes far beyond incidental utilization of real-life examples and is rather an intentional approach guiding curricular design (Valenzuela, 2018).

References

  • Adams, P., Gearhart, S., Miller, R., & Roberts, A. (2009). The accelerated learning program: Throwing open the gates. Journal of Basic Writing, 28, 50–69.

    Google Scholar 

  • Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. U.S. Department of Education. https://files.eric.ed.gov/fulltext/ED490195.pdf.

  • American Association of Community Colleges. (2018a). Fast facts. https://www.aacc.nche.edu/wp-content/uploads/2018/04/2018-Fast-Facts.pdf.

  • American Association of Community Colleges. (2018b). Faculty and staff diversity. https://www.aacc.nche.edu/wp-conteJunnt/uploads/2018/06/DataPoints_V6N7.pdf.

  • Ariovich, L., & Walker, S. A. (2014). Assessing course redesign: The case of developmental math. Research & Practice in Assessment, 9, 45–57.

    Google Scholar 

  • Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083–3107.

    Google Scholar 

  • Baber, L. D. (2018). “Living in the along”: Validating experiences among urban community college students in a college transition program. Community College Review, 46(3), 316–340.

    Google Scholar 

  • Bahr, P. R., Fagioli, L. P., Hetts, J., Hayward, C., Willett, T., Lamoree, D., Newell, M. A., Sorey, K., & Baker, R. B. (2019). Improving placement accuracy in California’s community colleges using multiple measures of high school achievement. Community College Review, 47(2), 178–211.

    Google Scholar 

  • Bailey, T. R. (2015). Redesigning America’s community colleges: A clearer path to student success. Cambridge: Harvard University Press.

    Google Scholar 

  • Baker, E. D., Hope, L., & Karandjeff, K. (2009). Contextualized teaching & learning: A promising approach for basic skills instruction. Research and Planning Group for California Community Colleges (RP Group). https://files.eric.ed.gov/fulltext/ED521932.pdf.

  • Baser, O. (2006). Too much ado about propensity score models? Comparing methods of propensity score matching. Value in Health, 9(6), 377–385.

    Google Scholar 

  • Belfield, C. R., Jenkins, D., & Fink, J. (2019). Early momentum metrics: Leading indicators for community college improvement (CCRC Research Brief.) Community College Research Center. https://ccrc.tc.columbia.edu/media/k2/attachments/early-momentum-metrics-leading-indicators.pdf.

  • Bettinger, E. P., & Long, B. T. (2005). Do faculty serve as role models? The impact of instructor gender on female students. American Economic Review, 95(2), 152–157.

    Google Scholar 

  • Bouillion, L. M., & Gomez, L. M. (2001). Connecting school and community with science learning: Real world problems and school–community partnerships as contextual scaffolds. Journal of Research in Science Teaching, 38(8), 878–898.

    Google Scholar 

  • Bryk, A. S., & Treisman, U. (2010). Make math a gateway, not a gatekeeper. Chronicle of Higher Education, 56(32), B19–B20.

    Google Scholar 

  • Burdman, P. (2012). Where to begin? The evolving role of placement exams for students starting college. Jobs for the Future. https://files.eric.ed.gov/fulltext/ED537265.pdf.

  • Calcagno, J. C., Crosta, P., 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.

    Google Scholar 

  • Chaves, C. (2006). Involvement, development, and retention: Theoretical foundations and potential extensions for adult community college students. Community College Review, 34(2), 139–152.

    Google Scholar 

  • Chen, X. (2016). Remedial coursetaking at US public 2-and 4-year institutions: Scope, experiences, and outcomes (NCES 2016–405). U.S. Department of Education. https://nces.ed.gov/pubs2016/2016405.pdf.

  • Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39–67.

    Google Scholar 

  • Cohen, A. M., Brawer, F. B., & Kisker, C. B. (2014). The American community college (6th ed.). Jossey-Bass.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

  • Cohen, R., & Kelly, A. M. (2019). The impact of community college science and mathematics coursetaking on graduation, transfer, and non-completion. The Review of Higher Education, 42(2), 595–617.

    Google Scholar 

  • Collier, D. A., & Parnther, C. (2021). Conversing with Kalamazoo promise scholars: An inquiry into the beliefs, motivations, and experiences of tuition-free college students. Journal of College Student Retention: Research, Theory & Practice, 22(4), 572–596.

    Google Scholar 

  • Cox, R. D. (2015). “You’ve got to learn the rules” A classroom-level look at low pass rates in developmental math. Community College Review, 43(3), 264–286.

    Google Scholar 

  • Crisp, G., & Delgado, C. (2014). The impact of developmental education on community college persistence and vertical transfer. Community College Review, 42(2), 99–117.

    Google Scholar 

  • Crisp, G., & Nuñez, A. M. (2014). Understanding the racial transfer gap: Modeling underrepresented minority and nonminority students’ pathways from two-to four-year institutions. The Review of Higher Education, 37(3), 291–320.

    Google Scholar 

  • Cuellar, M. G., & Gándara, P. (2020). Promoting access and equity for underrepresented racial minorities An examination of policies and practices in community college baccalaureate programs. Community College Review. https://doi.org/10.1177/0091552120964877

    Article  Google Scholar 

  • Deil-Amen, R. (2006). To teach or not to teach “social” skills: Comparing community colleges and private occupational colleges. Teachers College Record, 108(3), 397–421.

    Google Scholar 

  • Deil-Amen, R., & Rosenbaum, J. E. (2002). The unintended consequences of stigma free remediation. Sociology of Education, 75(3), 249–268.

    Google Scholar 

  • Diamond, A., & Sekhon, J. S. (2013). Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. Review of Economics and Statistics, 95(3), 932–945.

    Google Scholar 

  • Eagan, M. K., Jr., & Jaeger, A. J. (2009). Effects of exposure to part-time faculty on community college transfer. Research in Higher Education, 50(2), 168.

    Google Scholar 

  • Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. The Journal of the Learning Sciences, 8, 391–450.

    Google Scholar 

  • Fairlie, R. W., Hoffmann, F., & Oreopoulos, P. (2014). A community college instructor like me: Race and ethnicity interactions in the classroom. American Economic Review, 104(8), 2567–2591.

    Google Scholar 

  • Faust, J. L., & Paulson, D. R. (1998). Active learning in the college classroom. Journal on Excellence in College Teaching, 9(2), 3–24.

    Google Scholar 

  • Gillam, S. L., Gillam, R. B., & Reece, K. (2012). Language outcomes of contextualized and decontextualized language intervention: Results of an early efficacy study. Language, Speech, and Hearing Services in Schools, 43(3), 276–291.

    Google Scholar 

  • Grubb, W. N. (2001). From black box to Pandora’s box: Evaluating remedial/developmental education (CCRC Brief No. 11). Community College Research Center. https://ccrc.tc.columbia.edu/media/k2/attachments/black-box-evaluating-remedial-education.pdf.

  • Grubb, W. N. (2010). The quandaries of basic skills in community colleges: Views from the classroom (NCPR Working Paper). National Center for Postsecondary Research. https://files.eric.ed.gov/fulltext/ED533875.pdf.

  • Grubb, W. N. (2013). Basic skills education in community colleges: Inside and outside of classrooms. USA: Routledge.

    Google Scholar 

  • Grubb, W. N., Worthen, H., Byrd, B., Webb, E., Badway, N., Case, C., Goto, S., & Villeneuve, J. C. (1999). Honored but invisible: An inside look at teaching in community colleges. USA: Routledge.

    Google Scholar 

  • Hagedorn, L. S., & Kuznetsova, I. (2016). Developmental, remedial, and basic skills: Diverse programs and approaches at community colleges. New Directions for Institutional Research, 2015(168), 49–64.

    Google Scholar 

  • Hallett, R. E., & Freas, A. (2018). Community college students’ experiences with homelessness and housing insecurity. Community College Journal of Research and Practice, 42(10), 724–739.

    Google Scholar 

  • Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15(3), 234.

    Google Scholar 

  • Holmes, W. M. (2013). Using propensity scores in quasi-experimental designs. Sage Publications.

  • Jaeger, A. J. (2008). Contingent faculty and student outcomes. Academe, 94(6), 42–43.

    Google Scholar 

  • Jaeger, A. J., & Eagan, M. K., Jr. (2009). Unintended consequences: Examining the effect of part-time faculty members on associate’s degree completion. Community College Review, 36(3), 167–194.

    Google Scholar 

  • Jaggars, S. S., Hodara, M., Cho, S. W., & Xu, D. (2015). Three accelerated developmental education programs: Features, student outcomes, and implications. Community College Review, 43(1), 3–26.

    Google Scholar 

  • Jenkins, D., Brown, A. E., Fink, J., Lahr, H., & Yanagiura, T. (2018). Building guided pathways to community college student success: Promising practices and early evidence from Tennessee. Community College Research Center. https://ccrc.tc.columbia.edu/media/k2/attachments/building-guided-pathways-community-college-student-success.pdf.

  • Jenkins, P. D., Zeidenberg, M., & Kienzl, G. S. (2009). Educational outcomes of I-BEST, Washington State Community and Technical College system's integrated basic education and skills training program: Findings from a multivariate analysis (CCRC Working Paper No. 16). Community College Research Center. https://ccrc.tc.columbia.edu/media/k2/attachments/educational-outcomes-of-i-best.pdf.

  • King, G., & Nielsen, R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27(4), 1–20.

    Google Scholar 

  • Kosiewicz, H., & Ngo, F. (2020). Giving community college students choice: The impact of self-placement in math courses. American Educational Research Journal, 57(3), 1358–1391.

    Google Scholar 

  • Kosiewicz, H., Ngo, F., & Fong, K. (2016). Alternative models to deliver developmental math: Issues of use and student access. Community College Review, 44(3), 205–231.

    Google Scholar 

  • Laanan, F. S., & Jain, D. (2017). Advancing a new critical framework for transfer student research: Implications for institutional research. New Directions for Institutional Research, 2016(170), 9–21.

    Google Scholar 

  • Lancaster, J. R., & Lundberg, C. A. (2019). The influence of classroom engagement on community college student learning: A quantitative analysis of effective faculty practices. Community College Review, 47(2), 136–158.

    Google Scholar 

  • 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). Community College Research Center. http://ccrc.tc.columbia.edu/media/k2/attachments/longitudinal-data-momentum-point-research-tool.pdf.

  • Levin, H. M., & Calcagno, J. C. (2008). Remediation in the community college: An evaluator’s perspective. Community College Review, 35(3), 181–207.

    Google Scholar 

  • Lundy-Wagner, V. (2014, June 15–18). Developmental mathematics and the community college STEM pipeline. [Paper session]. American Society for Engineering Education Conference, Indianapolis, IN, United States.

  • Martin, K., Galentino, R., & Townsend, L. (2014). Community college student success: The role of motivation and self-empowerment. Community College Review, 42(3), 221–241.

    Google Scholar 

  • Mazzeo, C. (2008). Supporting student success at California community colleges: A white paper. Career Ladders Project for California Community Colleges.

  • Melguizo, T., Kosiewicz, H., Prather, G., & Bos, H. (2014). How are community college students assessed and placed in developmental math? Grounding our understanding in reality. Journal of Higher Education, 85(5), 691–722.

    Google Scholar 

  • Mesa, V., Celis, S., Suh, H., Lande, E., & Whittemore, T. (2011, May). Community college mathematics: Teaching, textbooks, and student understanding [Panel presentation]. Community College Interdisciplinary Research Forum’s Conference, Research and Innovation for 21st Century Students, University of Michigan Ann Arbor, MI, United States.

  • Moore, C., & Shulock, N. (2010). Divided we fail: Improving completion and closing racial gaps in California’s community colleges. Institute for Higher Education Leadership & Policy. https://files.eric.ed.gov/fulltext/ED513824.pdf.

  • National Council for Workforce Education. (2010). Breaking through: Contextualization toolkit. https://jfforg-prod-prime.s3.amazonaws.com/media/documents/BT_toolkit_June7.pdf.

  • Ngo, F. (2019). Fractions in college: How basic math remediation impacts community college students. Research in Higher Education, 60(4), 485–520.

    Google Scholar 

  • Ngo, F., & Kwon, W. W. (2015). Using multiple measures to make math placement decisions: Implications for access and success in community colleges. Research in Higher Education, 56(5), 442–470.

    Google Scholar 

  • Ngo, F., & Melguizo, T. (2020). The equity cost of inter-sector math misalignment: Racial and gender disparities in community college student outcomes. The Journal of Higher Education. https://doi.org/10.1080/00221546.2020.1811570

    Article  Google Scholar 

  • Park, E. S., Ngo, F., & Melguizo, T. (2020). The role of math misalignment in the community college STEM pathway. Research in Higher Education. https://doi.org/10.1007/s11162-020-09602-y

    Article  Google Scholar 

  • Park, R. S. E., & Scott-Clayton, J. (2018). The impact of Pell Grant eligibility on community college students’ financial aid packages, labor supply, and academic outcomes. Educational Evaluation and Policy Analysis, 40(4), 557–585.

    Google Scholar 

  • Parker, S., Traver, A. E., & Cornick, J. (2018). Contextualizing developmental math content into introduction to sociology in community colleges. Teaching Sociology, 46(1), 25–33.

    Google Scholar 

  • Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.

    Google Scholar 

  • Perin, D. (2011). Facilitating student learning through contextualization: A review of evidence. Community College Review, 39(3), 268–295.

    Google Scholar 

  • Perin, D. (2013). Literacy skills among academically underprepared students. Community College Review, 41(2), 118–136.

    Google Scholar 

  • President’s Council of Advisors on Science and Technology. (2012). Report to the president: Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pd.

  • Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.

    Google Scholar 

  • Quarles, C. L., & Davis, M. (2017). Is learning in developmental math associated with community college outcomes? Community College Review, 45(1), 33–51.

    Google Scholar 

  • R Core Team (2019). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/.

  • Ran, F. X., & Lin, Y. (2019). The Effects of Corequisite Remediation: Evidence from a Statewide Reform in Tennessee. CCRC Working Paper No. 115. Community College Research Center, Teachers College, Columbia University.

  • Rivet, A. E., & Krajcik, J. S. (2008). Contextualizing instruction: Leveraging students’ prior knowledge and experiences to foster understanding of middle school science. Journal of Research in Science Teaching, 45(1), 79–100.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Google Scholar 

  • Rubin, D. B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74(366a), 318–328.

    Google Scholar 

  • Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association, 95(450), 573–585.

    Google Scholar 

  • Rutschow, E. Z. (2019, March 18–19). The national academies of sciences, engineering, and medicine workshop on understanding success and failure of students in developmental mathematics: Developmental mathematics reforms [Workshop paper]. Board on Science Education, Washington, DC, United States. https://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_191791.pdf.

  • Sass, D. A., Castro-Villarreal, F., Wilkerson, S., Guerra, N., & Sullivan, J. (2018). A structural model for predicting student retention. The Review of Higher Education, 42(1), 103–135.

    Google Scholar 

  • Schudde, L. (2019). Short-and long-term impacts of engagement experiences with faculty and peers at community colleges. The Review of Higher Education, 42(2), 385–426.

    Google Scholar 

  • Schudde, L., & Keisler, K. (2019). The relationship between accelerated dev-ed coursework and early college milestones: Examining college momentum in a reformed mathematics pathway. AERA Open, 5(1), 2332858419829435.

    Google Scholar 

  • Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The matching package for R. Journal of Statistical Software, 42(7), 1–52.

    Google Scholar 

  • Shrum, J. L. (2015). Teacher’s handbook, contextualized language instruction. Cengage Learning.

  • Snyder, T. D., de Brey, C., & Dillow, S. A. (2016). Digest of education statistics 2014 (NCES 2016–006). U.S. Department of Education. https://nces.ed.gov/pubs2016/2016006.pdf.

  • Stout, R., Archie, C., Cross, D., & Carman, C. A. (2018). The relationship between faculty diversity and graduation rates in higher education. Intercultural Education, 29(3), 399–417.

    Google Scholar 

  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 25(1), 1–21.

    Google Scholar 

  • Stuart, E. A., (2020). Dealing with observed and unobserved effect moderators when estimating population average treatment effects. https://www.elizabethstuart.org/files/Stuart-MHRN-Generalizability-October2020.pdf.

  • Valenzuela, H. (2018). A multiple case study of college-contextualized mathematics curriculum. MathAMATYC Educator, 9(2), 49–55.

    Google Scholar 

  • van derKaay, C. D., & Young, W. H. (2012). Age-related differences in technology usage among community college faculty. Community College Journal of Research and Practice, 36(8), 570–579.

    Google Scholar 

  • Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (7th ed.). NY: Springer.

    Google Scholar 

  • Wang, X. (2017). Toward a holistic theoretical model of momentum for community college student success. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (pp. 259–308). Springer.

  • Wang, X. (2020). On my own: The challenge and promise of building equitable STEM transfer pathways. Cambridge: Harvard Education Press.

    Google Scholar 

  • Wang, X., & Wickersham, K. (2018). Women, community colleges, and STEM careers. New Directions for Institutional Research, 2018(179), 73–90.

    Google Scholar 

  • Wang, X., Sun, N., & Wickersham, K. (2017). Turning math remediation into “homeroom:” Contextualization as a motivational environment for community college students in remedial math. The Review of Higher Education, 40(3), 427–464.

    Google Scholar 

  • Wiseley, W. C. (2009). Effectiveness of contextual approaches to developmental math in California community colleges [Unpublished doctoral dissertation]. University of the Pacific.

  • Xu, D., & Dadgar, M. (2018). How effective are community college remedial math courses for students with the lowest math skills? Community College Review, 46(1), 62–81.

    Google Scholar 

  • Yamada, H., Bohannon, A. X., Grunow, A., & Thorn, C. A. (2018). Assessing the effectiveness of Quantway®: A multilevel model with propensity score matching. Community College Review, 46(3), 257–287.

    Google Scholar 

  • Yang, L., & Venezia, S. (2020). The impact of financial aid on associate degree attainment for rural community college students: A comparison of rural, urban, and suburban patterns. Community College Review, 48(4), 423–454.

    Google Scholar 

  • Zeidenberg, M., Cho, S. -W., & Jenkins, D. (2010). Washington State’s Integrated Basic Education and Skills Training program (I-BEST): New evidence of effectiveness (CCRC Working Paper No. 20). Community College Research Center. https://ccrc.tc.columbia.edu/media/k2/attachments/i-best-evidence-effectiveness.pdf.

  • Zhang, Y. L., Adamuti-Trache, M., & Connolly, J. (2019). From community college attendants to baccalaureate recipients: A planned behavior model for transfer students in STEM fields of study. The Journal of Higher Education, 90(3), 373–401.

    Google Scholar 

  • Zhang, Z., Kim, H. J., Lonjon, G., & Zhu, Y. (2019). Balance diagnostics after propensity score matching. Annals of Translational Medicine, 7(1), 16.

    Google Scholar 

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Funding

This study is based on work supported by the National Science Foundation under Grant No. DUE-1700625.

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  1. Department of Educational Leadership and Policy Analysis, University of Wisconsin-Madison, 270-H Education Building Bascom Mall, Madison, WI, 53706, USA

    Xueli Wang, Xiwei Zhu & Ayse Okur Ozdemir

  2. Wisconsin Center for Education Research, Madison, WI, USA

    Yen Lee

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Wang, X., Lee, Y., Zhu, X. et al. Exploring the Relationship Between Community College Students’ Exposure to Math Contextualization and Educational Outcomes. Res High Educ 63, 309–336 (2022). https://doi.org/10.1007/s11162-021-09644-w

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