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Examining Interventions to Reduce Stereotype Threat in Undergraduate Mathematics

Examining Interventions to Reduce Stereotype Threat in Undergraduate Mathematics. Dr. Jessica M. Deshler Department of Mathematics West Virginia University, USA. Randomised Controlled Trials in the Social Sciences, 9 th Annual Conference University of York, UK, September 2014.

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Examining Interventions to Reduce Stereotype Threat in Undergraduate Mathematics

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  1. Examining Interventions to Reduce Stereotype Threat in Undergraduate Mathematics Dr. Jessica M. Deshler Department of Mathematics West Virginia University, USA Randomised Controlled Trials in the Social Sciences, 9th Annual Conference University of York, UK, September 2014

  2. Context for the Study • My background: B.S., M.S. & Ph.D. in Mathematics • Research: applied mathematics  mathematics education • Approaching studies in the undergraduate classroom as both a researcher (mathematics, education) and an instructor.

  3. West Virginia University • Research Intensive, public, land-grant university • Morgantown, West Virginia • Fall 2013 Enrollment ~ 23,000 Undergraduate students ~ 5,000 Graduate students

  4. Stereotype Threat • Being at risk of confirming, as self-characteristic, a negative stereotype about one’s group. (Steele & Aronson, 1995) • Has been shown to reduce the performance of individuals who belong to negatively stereotyped groups. (Steele, 1997).

  5. Women & Mathematics “When women perform math, unlike men, they risk being judged by the negative stereotype that women have weaker math ability.” -(Spencer, Steele & Quinn, 1999)

  6. Why do we need to support women/girls in mathematics? • Diverse populations bring diverse perspectives – • Diversity Trumps Ability (Page, 2008) • If all of our students (employees, coworkers, etc.) have the same backgrounds (experiences, perspectives), they can’t work as efficiently to solve problems. • In US, 31% of PhDs in Mathematics (NSF, 2012) are women, but only 21% of tenure track faculty in mathematics departments.

  7. Our Study Interventions have been shown to reduce stereotype threat in a laboratory setting. • Research Question: Would the interventions work in an actual undergraduate mathematics classroom? Our implementation: Use two laboratory interventions in a college calculus class. Does the data support the laboratory findings?

  8. Values Affirmation Intervention • Students ranked 5 personal characteristics • Creativity, Humor, Physical Attractiveness, Social Skills & Relationships with friends/family • Describe importance of highest ranked characteristic • Describe a time in life this was important • Control: Rank same 5 characteristics but write about his/her least valuable attribute & why it’s important to others.

  9. Role Model Intervention • A reading on a fictitious female WVU student majoring in mathematics (and education) who has been successful in mathematics, college, etc…. • Picture • Control: A reading aboutbusiness/industry, not focused on specific people.

  10. Participants • Calculus I class - two sections • Students were not mathematics majors • 48 women, 76 men • One class period • (not at the institution where these were used in the lab) • Randomly Assigned to one of: • Control (no interventions) • Values Affirmation intervention ONLY • Role Model intervention ONLY • BOTH Interventions

  11. Design Randomly assigned, blocked by gender, to one of two conditions, both conditions, or neither condition.

  12. Plots by Intervention & Gender

  13. Means by Treatment: Female

  14. Means by Treatment: Male

  15. Procedural Results Female Male

  16. Conceptual Results Female Male

  17. Is this a real effect? • Concern about self-selection into the course • Sample size too small to detect the effects we saw (first time using an authentic measure) • Sample was chosen based on what worked in the lab – where the math test was GRE-like

  18. Characteristics of Participants

  19. Results – Continued

  20. Results - Continued

  21. Next Steps • Now that we have a sense of real performance on this assessment, run the experiment with a reasonable sample size. • Pay more careful attention to the randomization procedures, rather than having to fix problems later through the model • The experiment is cheap, relatively speaking (cost is in time) • Advice?

  22. References National Science Foundation. 2012. Women, Minorities, and Persons with Disabilities in Science and Engineering . Page, S. (2008). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies Steele, C. (1997). A threat in the air: How stereotypes shape intellectual identity and performance, American Psychologist, 52 (6) Steele, C.M. & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans, Journal of Personality and Social Psychology69 (5) Spencer, S.J., Steele, C.M. & Quinn, D.M., (1999). Stereotype Threat and Women’s Math Performance, Journal of Experimental Social Psychology, 35 (1)

  23. Acknowledgements Research Team: • Elizabeth Burroughs, Mathematics, Montana State University (Visiting at University of York) • Jessi Smith, Psychology, Montana State University • Rachel Matsumoto, Graduate student, Psychology, Montana State University

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