The DOI matches its Crossref record.
The Role of Math Misalignment in the Community College STEM Pathway
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 45
- Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.
- Allison, P. (2002). Missing data. (Sage University Papers Series on Quantitative Applications in the Social Sciences, Series no. 07–136). Thousand Oaks, CA: Sage. · DOI · Paper page
- Andrews, M., Schank, T., & Upward, R. (2006). Practical fixed-effects estimation methods for the three-way error-components model. Stata Journal, 6(4), 461–481. https://doi.org/10.1177/1536867X0600600402. · DOI
- Bahr, P. R., Jackson, G., McNaughtan, J., Oster, M., & Gross, J. (2017). Unrealized potential: Community college pathways to STEM baccalaureate degrees. The Journal of Higher Education, 88(3), 430–478. https://doi.org/10.1080/00221546.2016.1257313. · DOI
- Bahr, P. R., Fagioli, L. P., Hetts, J., Hayward, C., Willett, T., Lamoree, D., et al. (2019). Improving placement accuracy in California’s community colleges using multiple measures of high school achievement. Community College Review, 47(2), 178–211. https://doi.org/10.1177/0091552119840705. · DOI
- Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275. · DOI · Paper page
- 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. https://doi.org/10.1177/0091552115576566. · DOI
- 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. https://doi.org/10.3102/0002831209349460. · DOI · Paper page
- Crosta, P. (2014). Intensity and attachment: How the chaotic enrollment patterns of community college students relate to educational outcomes. Community College Review, 42(2), 118–142. https://doi.org/10.1177/0091552113518233. · DOI
- Duncheon, J. (2015). The problem of college readiness. In W. G. Tierney & J. Duncheon (Eds.), The problem of college readiness. Albany, NY: SUNY Press.
- Fong, K. E., & Melguizo, T. (2017). Utilizing additional measures of high school academic preparation to support students in their math selfassessment. Community College Journal of Research and Practice, 41(9), 566–592. https://doi.org/10.1080/10668926.2016.1179604. · DOI
- Goodman, J. (2019). The labor of division: Returns to compulsory high school math coursework. Journal of Labor Economics, 37(4), 1141–1182. https://doi.org/10.1086/703135. · DOI · Paper page
- Gottfried, M. A., & Bozick, R. (2016). Supporting the STEM pipeline: Linking applied STEM course-taking in high school to declaring a STEM major in college. Education Finance and Policy, 11(2), 177–202. https://doi.org/10.1162/EDFP_a_00185. · DOI
- Grubb, N., & Gabriner, R. (2013). Basic skills education in community colleges. Inside and outside the classrooms. New York: Routledge. · DOI
- Hagedorn, L., & 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. https://doi.org/10.1615/JWomenMinorScienEng.v16.i1.30. · DOI
- Hu, S., Bertrand Jones, T., Brower, R. L., Park, T., Tandberg, D., Nix, A., et al. (2015). Learning from the ground up: Developmental education reform at Florida College System institutions. Tallahassee, FL: Center for Postsecondary Success. · DOI
- Klasik, D., & Strayhorn, T. L. (2018). The complexity of college readiness: Differences by race and college selectivity. Educational Researcher. Advance online publication. https://doi.org/10.3102/0013189X18778598 · DOI
- Kosiewicz, H., & Ngo, F. (2019). Giving community college students choice: The impact of self-placement in math courses. American Educational Research Journal, 57(3), 1358–1391. https://doi.org/10.3102/0002831219872500. · DOI
- Lent, R. W., Brown, S. D., Schmidt, J., Brenner, B., Lyons, H., & Treistman, D. (2003). Relation of contextual supports and barriers to college choice behavior in engineering majors: Test of alternative social cognitive models. Journal of Counseling Psychology, 50(4), 458–465. https://doi.org/10.1037/0022-0167.50.4.458. · DOI
- Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907. https://doi.org/10.1002/sce.20441. · DOI
- Melguizo, T., & Ngo, F. (2020). Mis/Alignment between high school and community college standards. Educational Researcher, 49(2), 130–133. https://doi.org/10.3102/0013189X19898697. · DOI
- Melguizo, T., Kosiewicz, H., Prather, G., & Bos, J. (2014). How are community college students assessed and placed in developmental math? Grounding our understanding in reality. The Journal of Higher Education, 85(5), 691–722. https://doi.org/10.1080/00221546.2014.11777345. · DOI
- National Academies of Sciences, Engineering, and Medicine. (2016). Barriers and opportunities for 2-year and 4-year STEM degrees: Systemic change to support students' diverse pathways. Washington, DC: The National Academies Press.
- 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. https://doi.org/10.1007/s11162-014-9352-9. · DOI
- Ngo, F., Chi, W. E., & Park, E. S. (2018). Mathematics course placement using holistic measures: Possibilities for community college students. Teachers College Record, 120(2), 1–42. · DOI
- Porter, A. C., & Polikoff, M. S. (2012). Measuring academic readiness for college. Educational Policy, 26(3), 394–417. https://doi.org/10.1177/0895904811400410 · DOI
- Rabe-Hesketh, R., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata (3rd ed.). College Station, TX: Stata Press.
- 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. https://doi.org/10.3102/0013189X10391657. · DOI
- Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012). The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time. American Educational Research Journal, 49(6), 1048–1073. https://doi.org/10.3102/0002831211435229. · DOI
- Rodriguez, A. (2018). Inequity by design? Aligning high school math offerings and public flagship college entrance requirements. The Journal of Higher Education, 89(2), 153–183. https://doi.org/10.1080/00221546.2017.1341757. · DOI
- Rodriguez, R., Johnson, H., Mejia, M., & Brooks, B. (2017). Reforming math pathways at California’s community colleges. San Francisco, CA: Public Policy Institute of California.
- Rodriguez, O., Mejia, M., & Johnson, H. (2016). Determining college readiness in California’s community colleges: A survey of assessment and placement policies. San Francisco, CA: Public Policy Institute of California.
- Rodriguez, O., Mejia, M., & Johnson, H. (2018). Remedial education reform at California’s community colleges: Early evidence on placement and curricular reforms. San Francisco, CA: Public Policy Institute of California.
- Scott-Clayton, J., Crosta, P. M., & Belfield, C. R. (2014). Improving the targeting of treatment: Evidence from college remediation. Educational Evaluation and Policy Analysis, 36(3), 371–393. https://doi.org/10.3102/0162373713517935. · DOI
- Stinebrickner, T., & Stinebrickner, R. (2012). Learning about academic ability and the college dropout decision. Journal of Labor Economics, 30(4), 707–748. https://doi.org/10.1086/666525. · DOI
- Stinebrickner, R., & Stinebrickner, T. (2014). A major in science? Initial beliefs and final outcomes for college major and dropout. Review of Economic Studies, 81(1), 426–472. https://doi.org/10.1093/restud/rdt025. · DOI
- Tai, R., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143–1144. https://doi.org/10.1126/science.1128690. · DOI
- Valentine, J. C., Konstantopoulos, S., & Goldrick-Rab, S. (2017). What happens to students placed into developmental education? A meta-analysis of regression discontinuity studies. Review of Educational Research, 87(4), 806–833. https://doi.org/10.3102/0034654317709237. · DOI
- Vilorio, D. (2014). STEM 101: Intro to tomorrow’s jobs. Occupational Outlook Quarterly, 58(1), 2–12.
- 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. https://doi.org/10.3102/0002831213488622. · DOI · Paper page
- Wang, X. (2013). 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. https://doi.org/10.1007/s11162-013-9291-x. · DOI
- 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. https://doi.org/10.3102/0162373714552561. · DOI
- Wang, X. (2016). Course-taking patterns of community college students beginning in STEM: Using data mining techniques to reveal viable STEM transfer pathways. Research in Higher Education, 57(5), 544–569. https://doi.org/10.1007/s11162-015-9397-4. · DOI
- Wang, X. (2017). Toward a holistic theoretical model of momentum for community college student success. In Higher education: Handbook of theory and research (pp. 259–308). Springer, Cham. · DOI
- Wang, X., Sun, N., Lee, S. Y., & Wagner, B. (2017). Does active learning contribute to transfer intent among 2-year college students beginning in STEM? The Journal of Higher Education, 88(4), 593–618. https://doi.org/10.1080/00221546.2016.1272090. · DOI
Cited by
Sorted by citations
Promoting Achievement for Community College STEM Students through Equity-Minded Practices
Varty, A. K. (2022) · CBE—Life Sciences Education · doi:10.1187/cbe.21-09-0237 · Cited by 24
Career Guidance Advocacy Program and The Grade 10 Completers’ Career Choice
Maestrado, J. T., & Bucar, J. D. (2024) · International Journal of Multidisciplinary Applied Business and Education Research · doi:10.11594/ijmaber.05.02.12 · Cited by 5
Metadata
- Authors
- Park, E. S., Ngo, F., & Melguizo, T.
- Affiliations
- University of California, Irvine (US)
- University of Nevada, Las Vegas (US)
- University of Southern California (US)
- Corresponding author
- Park, Elizabeth S.
- Venue
- Research in Higher Education
- Published
- 2020-06-09
- Volume
- 62
- Issue
- 4
- Pages
- 403-447
- ISSN
- 0361-0365 (ISSN-L)
- 1573-188X
- OpenAlex
- W3035222549
- Cited by
- 17
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 1
- FWCI
- 11.20
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Research in Higher Education (publishedVersion)
- Digital Scholarship - UNLV (University of Nevada Reno) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (2015-1544254)
- Haynes Foundation (Dissertation Fellowship)
- USC Graduate School (Russell Endowed Fellowship)
- Topics
- Social Sciences › Education › Mathematics Education and Programs
- Social Sciences › Education › Higher Education Research Studies
- Social Sciences › Psychology › Applied Psychology › Career Development and Diversity
- Keywords
- developmental mathematics, STEM participation, transition to college
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review