Growth messages increase help-seeking and performance for women in STEM

Abstract

Women leave STEM majors at higher rates than men. This may be due to problematic messaging in gateway courses such as (a) mismatches between students’ cultural norms and those of the learning context and (b) fixed ideas about ability as unchangeable. The current study investigated how culturally relevant growth messages impacted help-seeking behavior and grades in a gateway STEM course. Undergraduate students (168 men, 285 women) were randomly assigned to receive an email invitation to a peer-led tutoring program that included factual information (control), growth messages, or culture-matching growth messages emphasizing effort and interdependence. Tutoring sign-ups, number of sessions attended, and grades were collected. Path analyses showed no impact of messaging on men’s sign-ups. Yet, growth messages increased women’s sign-ups relative to the control, which led to more sessions attended and, subsequently, higher course grades. Unexpectedly, the culture-matching growth condition decreased sign-ups for women. Follow-up exploratory survey data on students from the course (161 men, 268 women) revealed that more women perceived peer interactions as competitive than men, which may shed light on the unexpected findings. Programs should implement growth messages to recruit more women and to improve collaboration in peer-learning settings.

Keywords

academic help-seeking cultural matching gateway college courses growth mindset STEM women

Get full access to this article

View all access options for this article.

References

Aronson

Fried

C. B.

Good

(2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38, 113–125. doi:10.1006/jesp.2001.1491

Bang

Medin

(2010). Cultural processes in science education: Supporting the navigation of multiple epistemologies. Science Education, 94, 1008–1026. doi:10.1002/sce.20392

Barr

D. A.

Gonzalez

M. E.

Wanat

S. F.

(2008). The leaky pipeline: Factors associated with early decline in interest in premedical studies among underrepresented minority undergraduate students. Academic Medicine, 83, 503–511. doi:10.1097/ACM.0b013e31816bda16

Beede

D. N.

Julian

T. A.

Langdon

McKittrick

Khan

Doms

M. E.

(2011). Women in STEM: A gender gap to innovation (Issue Brief No. 04-11). Retrieved from https://files.eric.ed.gov/fulltext/ED523766.pdf

Bernstein

B. L.

Russo

N. F.

(2008). Explaining too few women in STEM careers: A psychosocial perspective. In Paludi

M. A.

(Ed.), The psychology of women at work: Challenges and solutions for our female workforce. Vol. 2: Obstacles and the identity juggle (pp. 1–33). Westport, CT: Praeger/Greenwood.

Blackwell

L. S.

Trzesniewski

K. H.

Dweck

C. S.

(2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263. doi:10.1111/j.1467-8624.2007.00995.x

Butler

(1998). Determinants of help seeking: Relations between perceived reasons for classroom help-avoidance and help-seeking behaviors in an experimental context. Journal of Educational Psychology, 90, 630–643. doi:10.1037/0022-0663.90.4.630

Cech

E. A.

Blair-Loy

(2010). Perceiving glass ceilings? Meritocratic versus structural explanations of gender inequality among women in science and technology. Social Problems, 57, 371–397. doi:10.1525/sp.2010.57.3.371

Ceci

S. J.

Williams

W. M.

(Eds.). (2007). Why aren’t more women in science?: Top researchers debate the evidence (1st ed.). Washington, DC: American Psychological Association.

10.

Chambers

D. W.

(1983). Stereotypic images of the scientist: The Draw-a-Scientist Test. Science Education, 67, 255–265. doi:10.1002/sce.3730670213

11.

Cheryan

Plaut

V. C.

(2010). Explaining underrepresentation: A theory of precluded interest. Sex Roles, 63, 475–488. doi:10.1007/s11199-010-9835-x

12.

Cheryan

Plaut

V. C.

Handron

Hudson

(2013). The stereotypical computer scientist: Gendered media representations as a barrier to inclusion for women. Sex Roles, 69, 58–71. doi:10.1007/s11199-013-0296-x

13.

Covarrubias

Herrmann

S. D.

Fryberg

S. A.

(2016). Affirming the interdependent self: Implications for Latino student performance. Basic and Applied Social Psychology, 38, 47–57. doi:10.1080/01973533.2015.1129609

14.

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, 924–942. doi:10.3102/0002831209349460

15.

Dai

Cromley

J. G.

(2014). The match matters: Examining student epistemic preferences in relation to epistemic beliefs about chemistry. Contemporary Educational Psychology, 39, 262–274. doi:10.1016/j.cedpsych.2014.06.002

16.

Dar-Nimrod

Heine

S. J.

(2006). Exposure to scientific theories affects women’s math performance. Science, 314, 435. doi:10.1126/science.1131100

17.

Diekman

A. B.

Brown

E. R.

Johnston

A. M.

Clark

E. K.

(2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science, 21, 1051–1057. doi:10.1177/0956797610377342

18.

Diekman

A. B.

Clark

E. K.

Johnston

A. M.

Brown

E. R.

Steinberg

(2011). Malleability in communal goals and beliefs influences attraction to STEM careers: Evidence for a goal congruity perspective. Journal of Personality and Social Psychology, 101, 902–918. doi:10.1037/a0025199

19.

Dweck

C. S.

(1999). Self-theories: Their role in motivation, personality, and development. New York, NY: Psychology Press.

20.

Dweck

C. S.

Master

(2009). Self-theories and motivation: Students’ beliefs about intelligence. In Wenzel

K. R.

Wigfield

(Eds.), Handbook of motivation at school (pp. 123–140). New York, NY: Routledge/Taylor & Francis Group.

21.

Ellis

Fosdick

B. K.

Rasmussen

(2016). Women 1.5 times more likely to leave STEM pipeline after calculus compared to men: Lack of mathematical confidence a potential culprit. PLoS ONE, 11, e0157447. doi:10.1371/journal.pone.0157447

22.

Farland

(2006). The effect of historical, nonfiction trade books on elementary students’ perceptions of scientists. Journal of Elementary Science Education, 18, 31–47. doi:10.1007/BF03174686

23.

Finson

K. D.

(2010). Drawing a scientist: What we do and do not know after fifty years of drawing. School Science and Mathematics, 102, 335–345. doi:10.1111/j.1949-8594.2002.tb18217.x

24.

Fischer

(2017). The downside of good peers: How classroom composition differentially affects men’s and women’s STEM persistence. Labour Economics, 46, 211–226. doi:10.1016/j.labeco.2017.02.003

25.

Fryberg

S. A.

Markus

H. R.

(2007). Cultural models of education in American Indian, Asian American and European American contexts. Social Psychology of Education, 10, 213–246. doi:10.1007/s11218-007-9017-z

26.

Garcia

Cohen

(2012). A social psychological perspective on educational intervention. In Shafir

(Ed.), Behavioral foundations of policy (pp. 329–350). Princeton, NJ: Princeton University Press.

27.

Gasiewski

J. A.

Eagan

M. K.

Garcia

G. A.

Hurtado

Chang

M. J.

(2012). From gatekeeping to engagement: A multicontextual, mixed method study of student academic engagement in introductory STEM courses. Research in Higher Education, 53, 229–261. doi:10.1007/s11162-011-9247-y

28.

Good

Aronson

Inzlicht

(2003). Improving adolescents’ standardized test performance: An intervention to reduce the effects of stereotype threat. Journal of Applied Developmental Psychology, 24, 645–662. doi:10.1037/a0026659

29.

Good

Rattan

Dweck

C. S.

(2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology: Attitudes and Social Cognition, 102, 700–717. doi:10.1037/a0026659

30.

Good

Rattan

Dweck

C. S.

(2018). Do I belong here? Middle school girls’ sense of belonging to math. Manuscript in preparation.

31.

Gosser

(2011). The PLTL boost: A critical review of research. Progressions: The Journal of Peer-Led Team Learning, 14, 4–19.

32.

Haley

J. M.

(2015). To curve or not to curve? The effect of college science grading policies on implicit theories of intelligence, perceived classroom goal structures, and self-efficacy (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (UMI No. 3688531)

33.

Henderson

Dweck

(1990). Achievement and motivation in adolescence: A new model and data. In Feldman

Elliott

(Eds.), At the threshold: The developing adolescent (pp. 308–329). Cambridge, MA: Harvard University Press.

34.

Herrmann

S. D.

Adelman

R. M.

Bodford

J. E.

Graudejus

Okun

M. A.

Kwan

V. S. Y.

(2016). The effects of a female role model on academic performance and persistence of women in STEM courses. Basic and Applied Social Psychology, 38, 258–268. doi:10.1080/01973533.2016.1209757

35.

Hill

Corbett

St. Rose

(2010). Why so few? Women in science, technology, engineering, and mathematics. Washington, DC: American Association of University Women.

36.

Jackson

M. C.

Galvez

Landa

Buonora

Thoman

D. B.

(2016). Science that matters: The importance of a cultural connection in underrepresented students’ science pursuit. Cell Biology Education, 15. doi:10.1187/cbe.16-01-0067

37.

Karabenick

S. A.

(1998). Strategic help seeking: Implications for learning and teaching. Mahwah, NJ: Erlbaum.

38.

Karabenick

S. A.

(2003). Seeking help in large college classes: A person-centered approach. Contemporary Educational Psychology, 28, 37–58. doi:10.1016/S0361-476X(02)00012-7

39.

Lilgendahl

J. P.

McAdams

D. P.

(2011). Constructing stories of self-growth: How individual differences in patterns of autobiographical reasoning relate to well-being in midlife. Journal of Personality, 79, 391–428. doi:10.1111/j.1467-6494.2010.00688.x

40.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149. doi:10.1037/1082-989X.1.2.130

41.

Magnusson

J. L.

Perry

R. P.

(1992). Academic help-seeking in the university setting: The effects of motivational set, attributional style, and help source characteristics. Research in Higher Education, 33, 227–245. doi:10.1007/BF00973580

42.

McLean

K. C.

Pratt

M. W.

(2006). Life’s little (and big) lessons: Identity statuses and meaning-making in the turning point narratives of emerging adults. Developmental Psychology, 42, 714–722. doi:10.1037/0012-1649.42.4.714

43.

Mervis

(2011). Weed-out courses hamper diversity. Science, 334, 1333. doi:10.1126/science.334.6061.1333

44.

Mohr

R. I.

Purdie-Vaughns

(2015). Diversity within women of color: Why experiences change felt stigma. Sex Roles, 73, 391–398. doi:10.1007/s11199-015-0511-z

45.

Murphy

(2018). Do perceptions of STEM professors’ mindsets influence women’s math performance? Manuscript in preparation.

46.

National Science Foundation, National Center for Science and Engineering Statistics. (2017). Women, minorities, and persons with disabilities in science and engineering: 2017 (Special Report NSF 17-310). Retrieved from www.nsf.gov/statistics/wmpd/

47.

Nosek

B. A.

Banaji

M. R.

Greenwald

A. G.

(2002). Math = male, me = female, therefore math not = me. Journal of Personality and Social Psychology, 83, 44–59. doi:10.1037//0022-3514.83.1.44

48.

Rosseel

(2012). lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48, 1–36. doi:10.18637/jss.v048.i02

49.

Rothwell

(2012, June 1). The need for more STEM workers [Web log message]. Retrieved from https://www.brookings.edu/blog/the-avenue/2012/06/01/the-need-for-more-stem-workers/

50.

Ryan

A. M.

Gheen

M. H.

Midgley

(1998). Why do some students avoid asking for help? An examination of the interplay among students’ academic efficacy, teachers’ social-emotional role, and the classroom goal structure. Journal of Educational Psychology, 90, 528–535. doi:10.1037/0022-0663.90.3.528

51.

Ryan

A. M.

Pintrich

P. R.

Midgley

(2001). Avoiding seeking help in the classroom: Who and why? Educational Psychology Review, 13, 93–114. doi:10.1023/A:1009013420053

52.

Seymour

Hewitt

N. M.

(1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.

53.

Smith

J. L.

Brown

E. R.

Thoman

D. B.

Deemer

E. D.

(2015). Losing its expected communal value: How stereotype threat undermines women’s identity as research scientists. Social Psychology of Education, 18, 443–466. doi:10.1007/s11218-015-9296-8

54.

Smith

J. L.

Cech

Metz

Huntoon

Moyer

(2014). Giving back or giving up: Native American student experiences in science and engineering. Cultural Diversity and Ethnic Minority Psychology, 20, 413–429. doi:10.1037/a0036945

55.

Smith

J. L.

Lewis

K. L.

Hawthorne

Hodges

S. D.

(2013). When trying hard isn’t natural: Women’s belonging with and motivation for male-dominated STEM fields as a function of effort expenditure concerns. Personality and Social Psychology Bulletin, 39, 131–143. doi:10.1177/0146167212468332

56.

Stephens

N. M.

Fryberg

S. A.

Markus

H. R.

Johnson

C. S.

Covarrubias

(2012). Unseen disadvantage: How American universities’ focus on independence undermines the academic performance of first-generation college students. Journal of Personality and Social Psychology, 102, 1178–1197. doi:10.1037/a0027143

57.

Stephens

N. M.

Hamedani

M. G.

Destin

(2014). Closing the social-class achievement gap: A difference-education intervention improves first-generation students’ academic performance and all students’ college transition. Psychological Science, 25, 943–953. doi:10.1177/0956797613518349

58.

Syed

Nelson

S. C.

(2015). Guidelines for establishing reliability when coding narrative data. Emerging Adulthood, 3, 375–387. doi:10.1177/2167696815587648

59.

Thoman

D. B.

Smith

J. L.

Brown

E. R.

Chase

Lee

J. Y. K.

(2013). Beyond performance: A motivational experiences model of stereotype threat. Educational Psychology Review, 25, 211–243. doi:10.1007/s10648-013-9219-1

60.

Thoman

D. B.

White

P. H.

Yamawaki

Koishi

(2008). Variations of gender–math stereotype content affect women’s vulnerability to stereotype threat. Sex Roles, 58, 702–712. doi:10.1007/s11199-008-9390-x

61.

Tobias

(1990). They’re not dumb, they’re different: Stalking the second tier. Tucson, AZ: Research Corporation.

62.

Tonso

K. L.

(1999). Engineering gender minus gendering engineering: A cultural model for belonging. Journal of Women and Minorities in Science and Engineering, 5, 365–405. doi:10.1615/JWomenMinorScienEng.v5.i4.60

63.

Yeager

D. S.

Walton

G. M.

(2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301. doi:10.3102/0034654311405999