1. GROWTH MINDSETS BUFFER FEMALES AGAINST IDENTITY THREAT IN COMPUTER SCIENCE JENI L. BURNETTE, NORTH CAROLINA STATE UNIVERSITY CRYSTAL L. HOYT, UNIVERSITY OF RICHMOND BARRY LAWSON, UNIVERSITY OF RICHMOND CAROL S. DWECK, STANFORD UNIVERSITY

2. COMPUTER SCIENCE JOBS

3. WOMEN IN COMPUTER SCIENCE A worsening gender gap pervades computing fields both in employment and in number of undergraduate degrees awarded (National Science Foundation, 2012). Employment: -17% of Google’s tech workers are women.15% at Facebook. -Similar stats can be found at most large tech companies. BS Degrees: -Fewer than 20% of CS degrees are awarded to women.

5. THE GENDER GAP IDENTITY THREAT: One reason for gender gaps is that women often find themselves threatened by the potential to confirm negative stereotypes associated with their gender; often termed “identity threat” (Steele et al., 2002). IMPLICIT THEORY: We suggest that a valuable perspective for overcoming threat and increasing interest and performance of females in CS in college is an implicit theory approach (Dweck, 2000; Molden & Dweck, 2006).

6. IMPLICIT THEORY APPROACH Piaget suggested that the development of meaning systems is just as important as logical thinking in shaping behavior. Carol Dweck and her colleagues proposed that implicit theories are an important belief system for understanding motivation, personality and development.

7. IMPLICIT THEORY APPROACH Entity Theory (Fixed Mindset) The belief that human qualities are relatively static Incremental Theory (Growth Mindset) The belief that human qualities can be cultivated Two Key Characteristics of Implicit Theories 1.Domain specific 2.Theories can change

8. MINDSETS & THREAT Interventions with adolescents -Growth mindset training boosted motivation and achievement (Blackwell et al., 2007). -Growth mindset training narrowed gender and race achievement gaps for 7 th grade students (Good et al., 2003). Intervention with college students -Growth mindset training narrowed racial achievement gaps (Aronson et al., 2002). Across these studies, students generally improved, although for students facing identity threat, the effects were often stronger.

9. CURRENT WORK I.Extended the implicit theory approach to the domain of CS to examine how growth mindsets buffer against the deleterious effects of threat. I.Investigated two reasons (why) identity threat is potentially detrimental to females’ interest and success in CS. -Females, when threatened, question their sense of belonging and thereby lose interest. -Females, when threatened, may disengage and thereby fail to reach their full potential for learning development. III.Used a new approach for encouraging growth mindsets.

10. STUDY 1 METHODS Participants Study 1: We recruited 1 st and 2 nd year college students (N = 220; women = 93). Measures Study 1: -Implicit Theory of Computer Science (  =.83). -“you can learn new things but you can’t really change your basic computer science ability” -Belonging (two items, r =.71). -“How much do you feel you belong in computer science” -Performance Identity Threat (  =.92). -“when performing computer science tasks, I worry that people will draw conclusions about my gender group based on my performance ” -Interest in CS (  =.95). -“I like computer science”

11. THEORETICAL MODEL STUDY 1 We first examined if growth theories buffer females against identity threat’s detrimental effects on sense of belonging. X Threat X Threat W Sex W Sex M Theory M Theory Y Belonging Y Belonging

12. RESULTS STUDY 1 Using mean centering for products and running PROCESS Model 3, two significant effects emerged: 1.Gender significantly predicted belonging, B = -.75, t(214) = -3.47, p <.01, with females reporting less sense of belonging than males. 2.The three way interaction was also statistically significant, B =.28 t(214) = 2.18, p <.05.

13. RESULTS STUDY 1 p =.04 p =.37 ns

14. MODERATED MEDIATION RESULTS STUDY 1

15. DISCUSSION STUDY 1 Findings: In summary, for females, a growth mindset of computer science buffers against the deleterious effects of identity threat on sense of belonging and subsequent interest. This effect holds controlling for implicit theories of intelligence, math and personality. Limitations: No causal evidence Self-report No performance data

16. STUDY 2 METHODS Participants: We recruited female college students (N = 102) Experimental Methods: 1. Participants watched a CS tutorial and completed a coding task. 2. Participants read either a fixed or growth article. 3. All participants received a failing “grade” on the task. 4. Students watched a tutorial where we further manipulated theories through feedback. 5. Students completed assessments. 6. Students provided with 6 opportunities to learn or click ahead. 7. Participants asked to complete one more coding task.

17. STUDY 2 METHODS Measures: -Implicit Theory of Computer Science (  =.92). -Belonging (two items, r =.49). -Interest in CS (  =.90). -Learning Engagement -We recorded the length of time (in seconds) that participants spent on the tutorial pages. -Performance -We created an improvement score by subtracting the time 1 task score from the time 2 task score so positive scores represent more improvement.

18. RESULTS STUDY 2: MANIPULATION CHECK t(102) = -4.39, p <.001

19. RESULTS STUDY 2: BELONGING & INTEREST Mindset Condition CS Interest Belonging.65**.60*** -.28, ns Indirect effect =.39; CI =.141 to.729

20. RESULTS STUDY 2: LEARNING & IMPROVEMENT Mindset Condition Improve Engagement 14.54* Indirect effect = 2.05; CI =.220 to 5.786.14** -3.75, ns * p =.06

21. Females in the growth, relative to fixed mindset, condition reported greater sense of belonging and subsequently interest in CS. Females in the growth, relative to the fixed mindset, condition spent more time engaged in the tutorial and subsequently showed greater improvement in CS coding tasks. DISCUSSION STUDY 2

22. INTERVENTION Building on these findings, we developed a growth mindset intervention for students. We had students watch four modules in intro CS classes at seven universities/colleges around the country (16 sections). N = 493 students (F = 144, M = 349) Control = 247 vs. Experimental = 246

23. INTERVENTION In the online learning modules, we had three main components 1.We taught a growth mindset of CS 2. We explained the research on mindsets and the potential value of holding a growth mindset for goal setting, operating and monitoring. 3. We offered a student tip for being successful in CS- a specific strategy offered by a female working at Google

24. CONCLUSIONS & FUTURE DIRECTIONS CONCLUSIONS The growth theory intervention in CS seemed to work equally well for both genders and slightly stronger for minorities. Primarily helped by buffering against the decline in growth theories across the semester. FUTURE DIRECTIONS Changing cultures of brilliance Targeting retention in community colleges

25. THANKS Collaborators: -Crystal Hoyt -Carol Dweck -Barry Lawson -Jeremy Bailenson Student researchers: -Julianne Gomez -Rachel Forsyth -Taylor Applebaum -Amy Shick -Sam Ostoich

26. QUESTIONS