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Abstract

Racially minoritized students in the United States constitute 30% of the U.S. population, but students from these populations represent a smaller proportion of those who earn science, technology, engineering, and mathematics (STEM) undergraduate degrees. This disproportionality contributes to race/ethnic income, status, and power inequalities linked to STEM careers. Using a combination of vote counting and narrative approaches, the authors synthesize 50 recent articles about the factors related to college students’ STEM participation. Consistent with cumulative disadvantage and critical race theories, findings reveal that the disproportionality of racially minoritized students in STEM is related to their inferior secondary school preparation; the presence of racialized lower quality educational contexts; reduced levels of psychosocial factors associated with STEM success; less exposure to inclusive and appealing curricula and instruction; lower levels of family social, cultural, and financial capital that foster academic outcomes; and fewer prospects for supplemental STEM learning opportunities. Policy implications of findings are discussed.

Keywords

STEM race ethnicity college

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References

Adamson

Darling-Hammond

(2012). Funding disparities and the inequitable distribution of teachers: Evaluating sources and solutions. Education Policy Analysis Archives, 20(37). https://doi.org/10.14507/epaa.v20n37.2012

*Arcidiacono

Aucejo

Hotz

(2013). University differences in the graduation of minorities in STEM fields: Evidence from California (NBER Working Paper No. 18799). https://doi.org/10.3386/w18799

Armstrong

M. A.

Jovanovic

(2017). The intersectional matrix: Rethinking institutional change for URM women in STEM. Journal of Diversity in Higher Education, 10(3), 216–231. https://doi.org/10.1037/dhe0000021

*Baird

M. D.

Buchinsky

Sovero

(2018). Decomposing the racial gap in STEM major attrition: A course-level investigation (RAND Working Paper Series WR-1171). https://www.rand.org/pubs/working_papers/WR1171.html

Bargh

(2007). Social psychology and the unconscious: The automaticity of higher mental processes. Psychology Press.

*Barker

Hovey

Thompson

(2014). Results of a large-scale, multi-institutional study of undergraduate retention in computing. In Proceedings of the 2014 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE.2014.7044267

Bean

Gnadt

Maupin

White

S. A.

Anderson

(2016). Mind the gap: Student researchers use secondary data to explore disparities in STEM education. Prairie Journal of Educational Research, 1(1), 32–54. https://doi.org/10.4148/2373-0994.1002

*Bonous-Hammarth

(2000). Pathways to success: Affirming opportunities for science, mathematics, and engineering majors. Journal of Negro Education, 69(1–2), 92–111.

*Bottia

Mickelson

Giersch

Stearns

Moller

(2018). The role of high school racial composition and opportunities to learn in students’ STEM college participation. Journal of Research in Science Teaching, 55(3), 446–476. https://doi.org/10.1002/tea.21426

10.

*Bottia

Stearns

Mickelson

Moller

Parker

(2015). The relationships among high school STEM learning experiences and students’ intent to declare and declaration of a STEM major in college. Teachers College Record, 117(3), 1–46. http://www.tcrecord.org/LIBRARY/PrintContent.asp?ContentID=17806

11.

Bottia

M. C.

(2019). Educational and residential segregation of immigrants in the United States. PRRAC Report. https://files.eric.ed.gov/fulltext/ED598425.pdf

12.

Bottia

M. C.

Stearns

Mickelson

R. A.

Moller

Jamil

(2020). The importance of community colleges in students’ choice to major in STEM. The Journal of Higher Education, 91(7), 1116–1148. https://doi.org/10.1080/00221546.2020.1742032

13.

Brown

B. A.

Henderson

Gray

Donovan

Sullivan

Patterson

Waggstaff

(2016). From description to explanation: An empirical exploration of the African-American pipeline problem in STEM. Journal of Research in Science Teaching, 53(1), 146–177. https://doi.org/10.1002/tea.21249

14.

*Burt

Williams

Smith

(2018). Into the storm: Ecological and sociological impediments to Black males’ persistence in engineering graduate programs. American Educational Research Journal, 55(5), 965–1006. https://doi.org/10.3102/0002831218763587

15.

*Cabrera

(2014). Latina/Latino students missing in STEM education: A case study (Publication No. 3582094) [Doctoral dissertation, D’Youville College]. ProQuest Dissertations and Theses Global. http://search.proquest.com/docview/1657375522/

16.

Carter

P. L.

(2006). Straddling boundaries: Identity, culture, and school. Sociology of Education, 79(4), 304–328. https://doi.org/10.1177/003804070607900402

17.

*Ceglie

Settlage

(2014). College student persistence in scientific disciplines: Cultural and social capital as contributing factors. International Journal of Science and Mathematics Education, 14(Suppl. 1), 169–186. https://doi.org/10.1007/s10763-014-9592-3

18.

*Chang

Cerna

Han

Saenz

(2008). The contradictory roles of institutional status in retaining underrepresented minorities in biomedical and behavioral science majors. Review of Higher Education, 31(4), 433–464. https://doi.org/10.1353/rhe.0.0011

19.

*Chang

Sharkness

Hurtado

Newman

(2014). What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups. Journal of Research in Science Teaching, 51(5), 555–580. https://doi.org/10.1002/tea.21146

20.

Charleston

L. J.

Adserias

R. P.

Lang

N. M.

Jackson

J. F. L.

(2014). Intersectionality and STEM: The role of race and gender in the academic pursuits of African American women in STEM. Journal of Progressive Policy & Practice, 2(3), 273–293. https://caarpweb.org/wp-content/uploads/2014/12/Charleston-Adserias-Lang-Jackson-2014.pdf

21.

Chetty

Hendren

Jones

M. R.

Porter

S. R.

(2018). Race and economic opportunity in the United States (NBER Working Paper No. 24441). https://www.nber.org/papers/w24441

22.

*Crisp

Nora

Taggart

(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

23.

Crisp

Taggart

Nora

(2015). Undergraduate Latina/o students: A systematic review of research identifying factors contributing to academic success outcomes. Review of Educational Research, 85(2), 249–274. https://doi.org/10.3102/0034654314551064

24.

Darling-Hammond

(2001). Inequality in teaching and schooling: How opportunity is rationed to students of color in America. In Smedley

B. D.

Stith

A. Y.

Colburn

Evans

(Eds.), The right thing to do: The smart thing to do (pp. 208–233). National Academy Press.

25.

Delgado

Stefancic

(2001). Critical race theory. New York University Press. https://doi.org/10.1093/acref/9780195301731.013.51089

26.

Denson

Chang

M. J.

(2009). Racial diversity matters: The impact of diversity-related student engagement and institutional context. American Educational Research Journal, 46(2), 322–353. https://doi.org/10.3102/0002831208323278

27.

DiPrete

T. A.

Eirich

G. M.

(2006). Cumulative advantage as a mechanism for inequality: A review of theoretical and empirical developments. Annual Review of Sociology, 32, 271–297. http://www.cs.jhu.edu/~misha/DIReadingSeminar/Papers/DiPrete05.pdf

28.

Edmunds

K. A.

Pearsall

Porterfield

L. K.

(2015). Narrowing pathways? Exploring the spatial dynamics of postsecondary STEM preparation in Philadelphia, Pennsylvania. Urban Review, 47(1), 1–25. https://doi.org/10.1007/s11256-014-0285-6

29.

*Engberg

Wolniak

(2013). College student pathways to the STEM disciplines. Teachers College Record, 115(1), 1–27.

30.

*Espinosa

(2011). Pipelines and pathways: Women of color in undergraduate STEM majors and the college experiences that contribute to persistence. Harvard Educational Review, 81(2), 209–241. https://doi.org/10.17763/haer.81.2.92315ww157656k3u

31.

Fenske

R. H.

Porter

J. D.

DuBrock

C. P.

(2000). Tracking financial aid and persistence of women, minority, and needy students in science, engineering, and mathematics. Research in Higher Education, 41(1), 67–94. https://doi.org/10.1023/A:1007042413040

32.

*Flynn

D. T.

(2016). STEM field persistence: The impact of engagement on postsecondary STEM persistence for underrepresented minority students. Journal of Educational Issues, 2(1), 185–214. https://doi.org/10.5296/jei.v2i1.9245

33.

Funk

Parker

(2018). Blacks in STEM jobs are especially concerned about diversity and discrimination in the workplace. Pew Research Center. https://www.pewsocialtrends.org/2018/01/09/blacks-in-stem-jobs-are-especially-concerned-about-diversity-and-discrimination-in-the-workplace/

34.

Gelbgiser

Alo

(2016). Math-oriented fields of study and the race gap in graduation likelihoods at elite colleges. Social Science Research, 58(July), 150–164. https://doi.org/10.1016/j.ssresearch.2016.03.005

35.

Gillborn

(2008). Racism and education: Coincidence or conspiracy? Routledge. https://doi.org/10.4324/9780203928424

36.

Green

Sanderson

(2018). The roots of STEM achievement: An analysis of persistence and attainment in STEM majors. The American Economist, 63(1), 79–93. https://doi.org/10.1177/0569434517721770

37.

*Griffith

(2010). Persistence of women and minorities in STEM field majors: Is it the school that matters? Economics of Education Review, 29(6), 911–922. https://doi.org/10.1016/j.econedurev.2010.06.010

38.

Grossman

J. M.

Porche

M. V.

(2014). Perceived gender and racial/ethnic barriers to STEM success. Urban Education, 49(6), 698–727. https://doi.org/10.1177/0042085913481364

39.

Gurin

Dey

Hurtado

Gurin

(2002). Diversity and higher education: Theory and impact on educational outcomes. Harvard Educational Review, 72(3), 330–367. https://doi.org/10.17763/haer.72.3.01151786u134n051

40.

*Hathaway

Nagda

Gregerman

(2002). The relationship of undergraduate research participation to graduate and professional education pursuit: An empirical study. Journal of College Student Development, 43(5), 614–631.

41.

Hill

A. J.

(2017). State affirmative action bans and STEM degree completions. Economics of Education Review, 57(April), 31–40. https://doi.org/10.1016/j.econedurev.2017.01.003

42.

*Hinojosa

Rapaport

Jaciw

LiCalsi

Zacamy

(2016). Exploring the foundations of the future STEM workforce: K-12 indicators of postsecondary STEM success (Regional Educational Laboratory Southwest Report REL 2016-122). https://eric.ed.gov/?id=ED565641

43.

Hoeg

D. G.

Bencze

J. L.

(2017). Values underpinning STEM education in the USA: An analysis of the Next Generation Science Standards. Science Education, 101(2), 278–301. https://doi.org/10.1002/sce.21260

44.

*Hughes

B. E.

Garibay

J. C.

Hurtado

Eagan

(2013). Examining the tracks that cause derailment: Institutional contexts and engineering degree attainments. AERA. http://www.heri.ucla.edu/nih/downloads/AERA-2013-Engineering-Completion.pdf

45.

*Jamil

(2017). At the intersection of relative risk aversion and effectively maintained inequality in STEM majors: A multilevel approach [Master’s thesis, University of North Carolina at Charlotte]. https://island1.uncc.edu/islandora/object/etd%3A1688

46.

*Johnson

J. D.

(2013). Predictors of Latina/o community college student vocational choice of STEM fields: Testing of the STEM-vocational choice model (Publication No. 3610652) [Doctoral dissertation, Iowa State University]. ProQuest Dissertations and Theses Global. http://search.proquest.com/docview/1500845522/

47.

Johnson

J. D.

Starobin

S. S.

Santos Laanan

(2016). Predictors of Latina/o community college student vocational choice in STEM. Community College Journal of Research and Practice, 40(12), 983–1000. https://doi.org/10.1080/10668926.2016.1204963

48.

Kokkelenberg

E. C.

Sinha

(2010). Who succeeds in STEM studies? An analysis of Binghamton University undergraduate students. Economics of Education Review, 29(6), 935–946. https://doi.org/10.1016/j.econedurev.2010.06.016

49.

Ladson-Billings

Tate

(1995). Toward a critical race theory of education. Teachers College Record, 97(1), 47–68.

50.

*Lee

(2015). An investigation of the linkage between technology-based activities and STEM major selection in 4-year postsecondary institutions in the United States: Multilevel structural equation modelling. Educational Research and Evaluation, 21(5–6), 439–465. https://doi.org/10.1080/13803611.2015.1093949

51.

Lee

S. J.

(2015). Unraveling the “model minority” stereotype: Listening to Asian American youth. Teachers College Press.

52.

Lee

V. E.

Burkam

D. T.

(2003). Dropping out of high school: The role of school organization and structure. American Educational Research Journal, 40(2), 353–393. https://doi.org/10.3102/00028312040002353

53.

Lichtenberger

George-Jackson

(2013). Predicting high school students’ interest in majoring in a STEM field: Insight into high school students’ postsecondary plans. Journal of Career and Technical Education, 28(1), 19–38. https://doi.org/10.21061/jcte.v28i1.571

54.

Loes

C. N.

Pascarella

E. T.

(2017). Collaborative learning and critical thinking: Testing the link. Journal of Higher Education, 88(5), 726–753. https://doi.org/10.1080/00221546.2017.1291257

55.

Lord

S. M.

Layton

R. A.

Ohland

M. W.

(2011). Trajectories of electrical engineering and computer engineering students by race and gender. IEEE Transactions on Education, 54(4), 610–618. https://doi.org/10.1109/TE.2010.2100398

56.

Lucas

S. R.

Berends

(2002). Sociodemographic diversity, correlated achievement, and de facto tracking. Sociology of Education, 75(4), 328–348. https://doi.org/10.2307/3090282

57.

*Ma

(2009). Family socioeconomic status, parental involvement, and college major choices: Gender, race/ethnic, and nativity patterns. Sociological Perspectives, 52(2), 211–234. https://doi.org/10.1525/sop.2009.52.2.211

58.

*Mau

W.-C. J.

(2016). Characteristics of US students that pursued a STEM major and factors that predicted their persistence in degree completion. Universal Journal of Educational Research, 4(6), 1495–1500. https://doi.org/10.13189/ujer.2016.040630

59.

McGee

. (2018). “Black genius, Asian fail”: The detriment of stereotype lift and stereotype threat in high-achieving Asian and Black STEM students. AERA Open, 4(4), 1–16. https://doi.org/10.1177/2332858418816658

60.

Mickelson

R. A.

(2015). The cumulative disadvantages of first-and second-generation segregation for middle school achievement. American Educational Research Journal, 52(4), 657–692. https://doi.org/10.3102/0002831215587933

61.

Mickelson

R. A.

Bottia

M. C.

(2021). K-16 STEM Archive. https://k16diversity.uncc.edu/stem-archive/

62.

Mickelson

R. A.

Bottia

M. C.

Lambert

(2013). Effects of school racial composition on K–12 mathematics outcomes: A metaregression analysis. Review of Educational Research, 83(1), 121–158. https://doi.org/10.3102/0034654312475322

63.

Miller

J. D.

Pearson

Jr. (2012). Pathways to STEMM professions for students from noncollege homes. Peabody Journal of Education, 87(1), 114–132. https://doi.org/10.1080/0161956X.2012.642277

64.

*Moller

Banerjee

Bottia

Stearns

Mickelson

Dancy

Wright

Valentino

(2015). Moving Latino/a students into STEM majors in college: The role of teachers and professional communities in secondary schools. Journal of Hispanic Higher Education, 14(1), 3–33. https://doi.org/10.1177/1538192714540533

65.

Morris

E. W.

Perry

B. L.

(2016). The punishment gap: School suspension and racial disparities in achievement. Social Problems, 63(1), 68–86. https://doi.org/10.1093/socpro/spv026

66.

Morrison

(2019, March 27). The college majors with the highest salaries: And the best prospects. Forbes. https://www.forbes.com/sites/nickmorrison/2019/03/27/the-college-majors-with-the-highest-salaries-and-the-best-prospects/?sh=30fc1f255aa2

67.

*Museus

Palmer

Davis

Maramba

(2011). Special issue: Racial and ethnic minority students’ success in STEM education. ASHE Higher Education Report, 36(6), 1–140. https://doi.org/10.1002/aehe.3606

68.

NACME. (2013). Scholarships for underrepresented minorities in STEM. https://www.nacme.org/underrepresented-minorities

69.

National Research Council. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering. National Academies Press.

70.

National Science Foundation. (2016). Integrated Postsecondary Education Data System: Completions survey. https://www.nsf.gov/statistics/srvyipeds/

71.

National Science Foundation’s Advance Program. (2001). ADVANCE at a glance. http://www.nsf.gov/crssprgm/advance/index.jsp

72.

*Nicholls

Wolfe

Besterfield-Sacre

Shuman

Larpkiattaworn

(2007). A method for identifying variables for predicting STEM enrollment. Journal of Engineering Education, 96(1), 33–44. https://doi.org/10.1002/j.2168-9830.2007.tb00913.x

73.

Nikischer

A. B.

(2013). Social class and the STEM career pipeline: An ethnographic investigation of opportunity structures in high-poverty versus affluent high school (Publication No. 3598726) [Doctoral dissertation, State University of New York at Buffalo]. ProQuest Dissertations and Theses Global. https://search.proquest.com/openview/a2c8a7d06f258960ccb31e0371f4a068/1?pq-origsite=gscholar&cbl=18750&diss=y

74.

*Niu

(2017). Family socioeconomic status and choice of STEM major in college: An analysis of a national sample. College Student Journal, 51(2), 298–312.

75.

*Ost

(2010). The role of peers and grades in determining major persistence in the sciences. Economics of Education Review, 29(6), 923–934. https://doi.org/10.1016/j.econedurev.2010.06.011

76.

Page

(2007). The difference. Princeton University Press.

77.

*Palmer

Maramba

Dancy

(2011). A qualitative investigation of factors promoting the retention and persistence of students of color in STEM. Journal of Negro Education, 80(4), 491–504.

78.

Parker

A. D.

(2013). Family matters: Familial support and science identity formation for African American female STEM majors [Doctoral dissertation, University of North Carolina at Charlotte]. https://core.ac.uk/download/pdf/345080417.pdf

79.

*Paschal

Taggart

(2019). An examination of the role of first-year college-level mathematics in STEM field major persistence at a Hispanic-serving institution. Journal of Hispanic Higher Education. Advance online publication. https://doi.org/10.1177/1538192719853464

80.

Peske

H. G.

Haycock

(2006). Teaching inequality: How poor and minority students are shortchanged on teacher quality. The Education Trust. https://eric.ed.gov/?id=ED494820

81.

Pew Research Center. (2015). National characteristics of public high schools attended by Hispanic, White and Black youth. https://www.pewresearch.org/hispanic/2005/11/01/iii-national-characteristics-of-public-high-schools-attended-by-hispanic-white-and-black-youth/

82.

Poon

Squire

Kodama

Byrd

Chan

Manzano

Furr

Bishundat

(2016). A critical review of the model minority myth in selected literature on Asian Americans and Pacific Islanders in higher education. Review of Educational Research, 86(2), 469–502. https://doi.org/10.3102/0034654315612205

83.

*Price

(2010). The effect of instructor race and gender on student persistence in STEM fields. Economics of Education Review, 29(6), 901–910. https://doi.org/10.1016/j.econedurev.2010.07.009

84.

*Rainey

Dancy

Mickelson

Stearns

Moller

(2018). Race and gender differences in how sense of belonging influences decisions to major in STEM. International Journal of STEM Education, 5(1), 1–14. https://doi.org/10.1186/s40594-018-0115-6

85.

Raymundo

(2020). Are Asian Americans POC? Examining impact of higher education identity-based policies and practices. Vermont Connection, 41(1), 5. https://scholarworks.uvm.edu/tvc/vol41/iss1/5/

86.

Reyna

V. F.

Benbow

C. P.

Boykin

A. W.

Whitehurst

G. R.

(2008). Report of the subcommittee on standards of evidence. Washington, DC: US Department of Education.

87.

Reynolds

Kizito

Ezumah

Mangesho

Allen

Chandler

(2011). Quality assurance of qualitative research: A review of the discourse. Health Research Policy and Systems, 9(1), Article 43. https://doi.org/10.1186/1478-4505-9-43

88.

Riegle-Crumb

Grodsky

(2010). Racial-ethnic differences at the intersection of math course-taking and achievement. Sociology of Education, 83(3), 248–270. https://doi.org/10.1177/0038040710375689

89.

*Riegle-Crumb

King

(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

90.

Riegle-Crumb

King

Grodsky

Muller

(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

91.

*Riegle-Crumb

King

Irizarry

(2019). Does STEM stand out? Examining racial/ethnic gaps in persistence across postsecondary fields. Educational Researcher, 48(3), 133–144. https://doi.org/10.3102/0013189X19831006

92.

Riegle-Crumb

Moore

Ramos-Wada

(2011). Who wants to have a career in science or math? Exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95(3), 458–476. https://doi.org/10.1002/sce.20431

93.

Rousseau

C. K.

Dixson

A. D.

(2006). The first day of school: A CRT story. In Dixson

A. D.

Anderson

C. K. R.

Donnor

J. K.

(Eds.), Critical race theory in education: All God’s children got a song (pp. 57–65). Routledge.

94.

*Russell

(2017, December). Can learning communities boost success of women and minorities in STEM? Evidence from the Massachusetts Institute of Technology. Economics of Education Review, 61(December), 98–111. https://doi.org/10.1016/j.econedurev.2017.10.008

95.

*Russell

Atwater

(2005). Traveling the road to success: A discourse on persistence throughout the science pipeline with African American students at a predominantly white institution. Journal of Research in Science Teaching, 42(6), 691–715. https://doi.org/10.1002/tea.20068

96.

*Sahin

Ekmekci

Waxman

(2018). Collective effects of individual, behavioral, and contextual factors on high school students’ future STEM career plans. International Journal of Science and Mathematics Education, 16(Suppl. 1), 69–89. https://doi.org/10.1007/s10763-017-9847-x

97.

Sampson

Garrison-Wade

D. F.

(2011). Cultural vibrancy: Exploring the preferences of African American children toward culturally relevant and non-culturally relevant lessons. Urban Review, 43(2), 279–309. https://doi.org/10.1007/s11256-010-0170-x

98.

*Sanchez

(2014). Persistence and performance for Latino community college students in STEM majors (Publication No. 3680562) [Doctoral dissertation, California State University]. ProQuest Dissertations and Theses Global. http://search.proquest.com/docview/1654429679/

99.

*Shebab

Walden

S. E.

Wellborn

(2015, June 14–17). Motivating factors for choosing engineering as reported by racial and ethnic minority students [Paper presentation]. ASEE Annual Conference and Expo, Seattle, Washington. https://www.asee.org/public/conferences/56/papers/11814/download

100.

Solórzano

Ceja

Yosso

(2000). Critical race theory, racial microaggressions, and campus racial climate: The experiences of African American college students. Journal of Negro Education, 69(1/2), 60–73.

101.

Solórzano

D. G.

(1998). Critical race theory, race and gender microaggressions, and the experience of Chicana and Chicano scholars. International Journal of Qualitative Studies in Education, 11(1), 121–136. https://doi.org/10.1080/095183998236926

102.

Solórzano

D. G.

Yosso

T. J.

(2002). Critical race methodology: Counter-storytelling as an analytical framework for education research. Qualitative Inquiry, 8(1), 23–44. https://doi.org/10.1177/107780040200800103

103.

Southworth

Mickelson

R. A.

(2007). The interactive effects of race, gender and school composition on college track placement. Social Forces, 86(2), 497–523. https://doi.org/10.1093/sf/86.2.497

104.

*Stearns

Bottía

Davalos

Mickelson

Moller

Valentino

(2016). Demographic characteristics of high school math and science teachers and girls’ success in STEM. Social Problems, 63(1), 87–110. https://doi.org/10.1093/socpro/spv027

105.

*Strayhorn

T. L.

(2015). Factors influencing black males’ preparation for college and success in STEM majors: A mixed methods study. Western Journal of Black Studies, 39(1), 45–63. http://search.proquest.com/docview/1688657629/

106.

*Strayhorn

T. L.

Long

L. L.

III Kitchen

J. A.

Williams

M. S.

Stentz

(2013). Academic and social barriers to Black and Latino male collegians’ success in engineering and related STEM fields [Proceedings]. 2013 ASEE Annual Conference and Exposition, Atlanta, GA. https://commons.erau.edu/publication/295

107.

*Toven-Lindsey

Levis-Fitzgerald

Barber

Hasson

(2015). Increasing persistence in undergraduate science majors: A model for institutional support of underrepresented students. CBE: Life Sciences Education, 14(2), 1–12. https://doi.org/10.1187/cbe.14-05-0082

108.

Tsapogas

(2004). The role of community colleges in the education of recent science and engineering graduates (InfoBrief NSF No. 04-315). National Science Foundation. https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf04315

109.

*Tyler

(2010). Examining the STEM educational pipeline: The influence of pre-college factors on the educational trajectory of African American students [Doctoral dissertation, Miami University]. https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?p10_etd_subid=57469&clear=10

110.

*Tyson

Lee

Borman

Hanson

(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 (JESPAR), 12(3), 243–270. https://doi.org/10.1080/10824660701601266

111.

U.S. Department of Commerce. (2018). College enrollment rates. https://nces.ed.gov/programs/coe/pdf/coe_cpb.pdf

112.

U.S. Department of Education. (2018). Public high school graduation rates (Office of Elementary and Secondary Education, Consolidated State Performance Report No. 2016–17). https://nces.ed.gov/programs/coe/indicator_coi.asp

113.

Velez-Ibanez

C. G.

Greenberg

J. B.

(1992). Schooling processes among U.S. Mexicans, Puerto Ricans, and Cubans: A comparative, distributive, and case study approach. ERIC Clearinghouse. https://eric.ed.gov/?id=ED347022

114.

Wagstaff

I. R.

(2014). Predicting 9th graders’ science self-efficacy and STEM career intent: A multilevel approach [Doctoral dissertation, North Carolina State University]. https://repository.lib.ncsu.edu/handle/1840.16/9389

115.

Wang

(2013a). 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

116.

*Wang

(2013b). 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

117.

Welsh

R. O.

Little

(2018). The school discipline dilemma: A comprehensive review of disparities and alternative approaches. Review of Educational Research, 88(5), 752–794. https://doi.org/10.3102/0034654318791582

118.

Whalen

D. F.

Shelley

M. C.

(2010). Academic success of STEM and non-STEM majors. Journal of STEM Education, 11(1–2), 45–60. https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1016&context=pols_pubs

119.

Wolfmeyer

Lupinacci

Chesky

(2017). Three ontologies of STEM education: An apolitical curricular trend, Eurocentric economic policy, and discursive episteme. Critical Education, 8(15), 69–79. https://doi.org/10.14288/ce.v8i15.186337

120.

*Xie

Fang

Shauman

(2015). STEM education. Annual Review of Sociology, 41(1), 331–357. https://doi.org/10.1146/annurev-soc-071312-145659

121.

Yosso

T. J.

(2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91. https://doi.org/10.1080/1361332052000341006

122.

*You

(2013). Gender and ethnic differences in precollege mathematics coursework related to science, technology, engineering, and mathematics (STEM) pathways. School Effectiveness and School Improvement, 24(1), 64–86. https://doi.org/10.1080/09243453.2012.681384

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Factors Associated With College STEM Participation of Racially Minoritized Students: A Synthesis of Research

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