The Impact of Participating in College-Run STEM Clubs and Programs on Students’ STEM Career Aspirations

Abstract

Background:

The United States continues to invest considerable resources into developing the next generation of science, technology, engineering, and mathematics (STEM) talent. Efforts to shore up interest in pursuing STEM careers span decades and have increasingly focused on boosting interest among diverse student populations. Policymakers have called for engaging students in a greater STEM ecology of support that extends beyond the traditional classroom environment to increase student STEM career interest. Yet, few robust studies exist exploring the efficacy of many programmatic efforts and initiatives outside the regular curriculum intended to foster STEM interest. To maximize STEM education investments, promote wise policies, and help achieve the aim of creating STEM learning ecosystems that benefit diverse student populations and meet the nation’s STEM goals, it is crucial to examine the effectiveness of these kinds of STEM education initiatives in promoting STEM career aspirations.

Purpose:

The purpose of this quasi-experimental study was to examine the impact of one popular, yet understudied, STEM education initiative on students’ STEM career aspirations: participation in a university- or college-run STEM club or program activity (CPA) during high school. Specifically, we studied whether participation in a college-run STEM CPA at a postsecondary institution during high school was related to college-going students’ STEM career aspirations, and we examined whether that relationship differed depending on student characteristics and prior STEM interests.

Research Design:

We conducted a quasi-experimental investigation to explore the impact of participation in university- or college-run STEM CPAs on college-going students’ STEM career aspirations. We administered a retrospective cohort survey to students at 27 colleges and universities nationwide resulting in a sample of 15,847 respondents. An inverse probability of treatment weighted logistic regression model with a robust set of controls was computed to estimate the odds of expressing STEM career aspirations among those who participated in college-run STEM CPAs compared with the odds expressed among students who did not participate. Our weighting accounted for self-selection effects.

Results:

Quasi-experimental modeling results indicated that participation in university- or college-run STEM CPAs had a significant impact on the odds that college-going students would express STEM career aspirations relative to students who did not participate. The odds of expressing interest in a STEM career among participants in STEM CPAs were 1.49 times those of the control group. Robustness checks confirmed our results. The result held true for students whether or not they expressed interest in STEM careers prior to participation in STEM CPAs, and it held true across a diverse range of student characteristics (e.g., race, parental education, gender, standardized test scores, and family/school encouragement).

Conclusions:

Results suggest that university- and college-run STEM CPAs play an important role in the STEM education ecology, serving the national goal of expanding the pool of college-going students who aspire to STEM careers. Moreover, results showed that participation in university- and college-run STEM CPAs during high school is equally effective across diverse student characteristics. Policymakers, educators, and those charged with making investment decisions in STEM education should seriously consider university- and college-run STEM CPAs as a promising vehicle to promote diverse students’ STEM career aspirations in the broader STEM learning ecosystem and as an important complement to other STEM learning environments.

Keywords

STEM college career aspirations STEM education ecology STEM clubs and programs

Get full access to this article

View all access options for this article.

References

Allen

Chang

Gorrall

Waggenspack

Fukuda

Little

Noam

(2019). From quality to outcomes: A national study of afterschool STEM programming. International Journal of STEM Education, 37, 1–21.

Astin

(1971). The pre-college student science training program of the National Science Foundation: An empirical analysis. The Journal of Experimental Education, 39(4), 1–12.

Austin

(2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.

Bell

Lewenstein

Shouse

Feder

(2009). Learning science in informal environments: People, places, and pursuits. The National Academies Press, Committee on Learning Science in Informal Environments, National Research Council.

Bhattacharyya

Mead

Nathaniel

(2011). The influence of science summer camp on African-American high school students’ career choices. School Science and Mathematics, 111(7), 345–353.

Boedeker

Bicer

Capraro

Morgan

Barroso

(2015). STEM summer camp follow-up study: Effects on students’ SAT scores and postsecondary matriculation [Conference session]. 2015 IEEE Frontiers in Education Conference, El Paso, TX, United States.

Bradburn

(2000). Temporal representation and event dating. In Stone

A. A.

Turkan

J. S.

Bachrach

C. A.

Jobe

J. B.

Kurtzman

H. S.

Cain

V. S.

(Eds.), The science of self-report (pp. 49–61). Lawrence Erlbaum.

Bureau of Labor Statistics. (2019). Employment in STEM occupations. https://www.bls.gov/emp/tables/stem-employment.htm

Carnevale

Cheah

Hanson

(2015). The economic value of college majors. Georgetown University Center on Education and the Workforce.

10.

Cole

J. S.

Gonyea

R. M.

(2010). Accuracy of self-reported SAT and ACT test scores: Implications for research. Research in Higher Education, 51(4), 305–319.

11.

Committee on STEM Education. (2018). Charting a course for success: America’s strategy for STEM education. National Science and Technology Council. https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf

12.

Constan

Spicer

(2015). Maximizing future potential in physics and STEM. Journal of Higher Education Outreach and Engagement, 19(2), 117–135.

13.

Cooley

Bassett

(1961). Evaluation and follow-up study of a summer science and mathematics program for talented secondary school students. Science Education, 45(3), 209–216.

14.

Corin

Jones

Andre

Childers

(2018). Characteristics of lifelong science learning: An investigation of STEM hobbyists. International Journal of Science Education, Part B, 8(1), 53–75.

15.

Corin

Sonnert

Sadler

(2020). The role of dual enrollment STEM coursework in increasing STEM career interest among American high school students. Teachers College Record, 122(2), 1–26.

16.

Dabney

Tai

Almarode

Miller-Friedmann

Sonnert

Sadler

Hazari

(2012). Out-of-school time science activities and their association with career interest in STEM. International Journal of Science Education, Part B, 2(1), 63–79.

17.

Donnelly

Diaz

Hernandez

(2016). OCTET and BIOTEC: A model of summer intensive camp designed to cultivate the future generation of young leaders in STEM. Journal of Chemical Education, 93, 619–625.

18.

Engberg

Wolniak

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

19.

Estrada

Burnett

Campbell

Denetclaw

Gutierrez

Hurtado

John

(2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(5), 1–10.

20.

Fields

(2009). What do students gain from a week at science camp? Youth perceptions and the design of an immersive, research-oriented astronomy camp. International Journal of Science Education, 31(2), 151–171.

21.

Garrido

M. M.

Kelley

A. S.

Paris

Roza

Meier

D. E.

Morrison

R. S.

Aldridge

M. D.

(2014). Methods for constructing and assessing propensity scores. Health Services Research, 49(5), 1701–1720.

22.

Holmes

(2014). Using propensity scores in quasi-experimental designs. SAGE.

23.

Hong

(2010). Marginal mean weighting through stratification: Adjustment for selection bias in multilevel data. Journal of Educational and Behavioral Statistics, 35(5), 499–531.

24.

Houser

Garcia

Torres

(2015). Effectiveness of geosciences exploration summer program for increasing awareness and knowledge of geosciences. Journal of Geoscience Education, 63(2), 116–126.

25.

Jahn

Myers

(2014). Vocational anticipatory socialization of adolescents: Messages, sources, and frameworks that influence interest in STEM careers. Journal of Applied Communication Research, 42(1), 85–106.

26.

Jahn

Myers

(2015). When will I use this? How math and science classes communicate impressions of STEM careers: Implications for vocational anticipatory socialization. Communication Studies, 66(2), 218–237.

27.

Johnson

(2016). Managing transitions, building bridges: An evaluation of a summer bridge program for African American scientists and engineers. Journal for Multicultural Education, 10(2), 206–216.

28.

Jones

Childers

Corin

Chesnutt

Andre

(2019). Free choice science learning and STEM career choice. International Journal of Science Education, Part B, 9(1), 29–39.

29.

Jones

Taylor

Forrester

(2011). Developing a scientist: A retrospective look. International Journal of Science Education, 33(12), 1653–1673.

30.

Katzenmeyer

Lawrenz

(2006). National Science Foundation perspectives on the nature of STEM program evaluation. New Directions for Evaluation, 109, 7–18.

31.

Kezar

(Ed.). (2010). Recognizing and serving low-income students in postsecondary education. Routledge.

32.

Kitchen

J. A.

Kezar

Hypolite

(2021). At-promise college student major and career self-efficacy ecology model. Journal of Diversity in Higher Education. Advance online publication. https://doi.org/10.1037/dhe0000324

33.

Kitchen

J. A.

Sadler

Sonnert

(2018). The impact of summer bridge programs on college students’ STEM career aspirations. Journal of College Student Development, 59(6), 698–715.

34.

Kitchen

J. A.

Sonnert

Sadler

(2018). The impact of college- and university-run high school summer programs on students’ end of high school STEM career aspirations. Science Education, 102(3), 529–547.

35.

Kitchen

J. A.

Sonnert

Sadler

(2020). Campus visits: The impact of a college outreach strategy on student science, technology, engineering, and mathematics (STEM) aspirations. Journal of Student Affairs Research and Practice, 57(3), 266–281.

36.

Krishnamurthi

Ballard

Noam

(2014). Examining the impact of afterschool STEM programs. The Noyce Foundation. https://files.eric.ed.gov/fulltext/ED546628.pdf

37.

Kuncel

Credé

Thomas

(2005). The validity of self-reported grade point averages, RICs, and test scores: A meta-analysis and review of the literature. Review of Educational Research, 75(1), 63–82.

38.

Leite

(2017). Propensity score methods using R. SAGE.

39.

Lent

Brown

Hackett

(1994). Toward a unifying social cognitive theory of career and academic interest, choice. Journal of Vocational Behavior, 45, 79–122.

40.

Linden

Adams

(2012). Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 18, 317–325.

41.

Medina

(2019). Demographic turning points for the United States: Population projections from 2020-2060. U.S. Census Bureau. https://www2.census.gov/news/press-kits/2019/so-demographers-assoc-meeting/presentations/demographic-turning-points-for-the-us-population-projections.pdf

42.

Mielke

Butler

(2013). 4-H science initiative: Youth engagement, attitudes, and knowledge study. Policy Studies Associates. https://4-h.org/wp-content/uploads/2016/02/Year-3-YEAK-Full-Report.pdf

43.

Miller

Sonnert

Sadler

(2018). The influence of students’ participation in STEM competitions on their interest in STEM careers. International Journal of Science Education, Part B, 8(2), 95–114.

44.

Myers

Jahn

Gaillard

Stolzfus

(2011). Vocational anticipatory socialization (VAS): A communicative model of adolescents’ interests in STEM. Management Communication Quarterly, 25(1), 87–120.

45.

National Research Council. (2015). Identifying and supporting productive STEM programs in out-of-school settings. The National Academies Press.

46.

Niemi

Smith

(2003). The accuracy of students’ reports of course taking in the 1994 National Assessment of Educational Progress. Educational Measurement, 22(1), 15–21.

47.

Nugent

Barker

Grandgenett

Welch

(2016). Robotics camps, clubs, and competitions: Results from a US robotics project. Robotics and Autonomous Systems, 75, 686–691.

48.

Oetting

Beauvais

(1990). Adolescent drug use: Findings of national and local surveys. Journal of Consulting and Clinical Psychology, 58, 385–394.

49.

Osborne

Simon

Collins

(2003). Attitudes toward science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079.

50.

Pace

Barahona

Kaplan

(1985). The credibility of student self-reports. UCLA Center for the Study of Evaluation.

51.

President’s Council of Advisors on Science and Technology. (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Report to the President.

52.

Roberts

Wassersug

(2009). Does doing scientific research in high school correlate with students staying in science? A half century retrospective study. Research in Science Education, 39, 251–256.

53.

Rosenbaum

Rubin

(1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

54.

Rubin

(1987). Multiple imputation for nonresponse in surveys. Wiley.

55.

Sahin

Gulacar

Stuessy

(2015). High school students’ perceptions of the effects of International Science Olympiad on their STEM career aspirations and twenty-first century skill development. Research in Science Education, 45(6), 785–805.

56.

Sanchez

Buddin

(2015, April). How accurate are self-reported high school courses, grades, and grade point average? ACT Working Paper Series. ACT.

57.

Schiel

Noble

(1991, December). Accuracy of self-reported course work and grade information of high school sophomores. 91-6 ACT.

58.

Schmidt

Kelter

(2017). Science fairs: A qualitative study of their impact on student science inquiry learning and attitudes toward STEM. Science Educator, 25(2), 126–132.

59.

Sims

(1992). What went wrong: Why programs failed. Science, 258, 1185–1187.

60.

Stolle-McAllister

(2011). The case for summer bridge: Building social and cultural capital for talented Black STEM students. Science Educator, 20(2), 12–22.

61.

Strayhorn

(2012). College students’ sense of belonging: A key to educational success for all students. Routledge.

62.

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 [Conference session]. Proceedings from 2013 ASEE annual conference and exposition, Atlanta, GA, United States.

63.

Super

(1953). A theory of vocational development. American Psychologist, 8, 185–190.

64.

Thoemmes

Ong

(2016). A primer on inverse probability of treatment weighting and marginal structural models. Emerging Adulthood, 4(1), 40–59.

65.

Thorndike

(1997). Measurement and evaluation in psychology and education. Prentice Hall.

66.

Tomasko

Ridgway

Waller

Olesik

(2016). Association of summer bridge program outcomes with STEM retention of targeted demographic groups. Journal of College Science Teaching, 45(4), 90–99.

67.

U.S. Commission on Civil Rights. (2018). Public education funding inequity: In an era of increasing concentration of poverty and re-segregation. https://www.usccr.gov/pubs/2018/2018-01-10-Education-Inequity.pdf

68.

U.S. Department of Education. (2019). STEM investments FY18-20. https://www.ed.gov/sites/default/files/documents/stem/stem-investments-18-20.pdf

69.

Woolston

Zaki

Winter

(1997). The effectiveness of freshman recruiting strategies: An analysis at the University of Wisconsin-Madison [Conference session]. Proceedings of the Frontiers in Education Conference, Pittsburgh, PA, United States.

70.

Young

Ortiz

Young

(2017). STEMulating interest: A meta-analysis of the effects of out-of-school time on student STEM interest. International Journal of Education in Mathematics, Science, and Technology, 5(1), 62–74.

71.

Young

(2018). We can achieve if we receive: Examining the effects of out of school time activities on Black student achievement in mathematics. Equity & Excellence in Education, 51(2), 182–198.