1. STEM Education for All: Why Doesn’t this Yet Compute? Shirley M. Malcom, Ph.D.

2. A History of STEM Participation Spotty before WWII Women’s wartime opportunities (the first “computers”— “Men built the machines, but women made them work”) Access to education for minorities (separate and unequal; not “science material”) STEM Education for All: Why Doesn’t this Yet Compute?

3. The Post-Sputnik Push NDEA and financial support for STEM study Teacher preparation New curricula Science experiences “Incidental inclusion” STEM Education for All: Why Doesn’t this Yet Compute?

4. Structural Barriers in STEM Education Segregation of schools and under-resourcing of schools serving URM students -Barriers to students w/ disabilities in schools -Curricular options Cultural assumptions re: capacity and/or interest Program segregation (home ec vs. shop; “Girls High) STEM Education for All: Why Doesn’t this Yet Compute?

5. The Legal and Judicial Battles for Access Brown vs. Board of Education of Topeka, Kansas (1954) Titles VI and VII (1964) Executive Order 11246 (1965) Title IX (1972) Section 504 (1973) DeFunis vs. Odegaard (1974) Regents of the University of California vs. Bakke STEM Education for All: Why Doesn’t this Yet Compute?

6. The Legal and Judicial Battles for Access (cont’d) Science and Engineering Equal Opportunities Act (1980) Americans with Disabilities Act (1990) Adarand Contractors vs. Peña (1995) Grutter vs. Bollinger; Gratz vs. Bollinger (2003) Fisher vs. University of Texas-Austin (pending) Various state ballot initiatives STEM Education for All: Why Doesn’t this Yet Compute?

7. Cultural Battles for Access Civil rights movement Women’s movement Disability rights movement More “nuanced” movements within education (First Gen, minority males) STEM Education for All: Why Doesn’t this Yet Compute?

8. Historical Approaches and Interventions in the Out of School Space Mathematics as a “critical filter” 1973 Overcoming Math Anxiety 1978 Expanding Your Horizons (Math/Science Network) 1974 MESA 1970; MEP 1973 National Advisory Council on Minorities in Engineering 1974 Minorities in Engineering: A Blueprint for Action AAAS Project on the Handicapped in Science 1975 STEM Education for All: Why Doesn’t this Yet Compute?

9. What We Learned about STEM Education for All from Out of School Programs (from Equity and Excellence: Compatible Goals) Strong academic component in math, science, communications Highly qualified teachers who believe students can learn Emphasis on applications and career connections Interdisciplinary with hands-on opportunities, incorporation of computing Multi-year involvement Strong leadership with stable, committed staff STEM Education for All: Why Doesn’t this Yet Compute?

10. What We Learned from Out of School Programs (cont’d) Stable funding base, multiple sources Broad recruitment Multi-sector cooperation Opportunities for in-and out-of-school learning Parental involvement/community support Specific attention to race/gender related inequalities Professionals and staff who look like students Peer support systems/ no “tokens” Evaluation, follow-up, data collection “Mainstreaming” into institutional programs STEM Education for All: Why Doesn’t this Yet Compute?

11. What’s Needed In School and Out of School? A systems approach A clear vision Evidence-based strategies Content, Context, Culture and Community STEM Education for All: Why Doesn’t this Yet Compute?

12. A System of Solutions For want of a nail the shoe was lost. For want of a shoe the horse was lost. For want of a horse the rider was lost. For want of a rider the battle was lost. For want of a battle the kingdom was lost. And all for the want of a horseshoe nail.

13. Why Doesn’t this Yet Compute? Schools that don’t work for all students (Belief, behavior, practice, policy) Accountability without support,using imperfect standards (e.g., teaching to bad tests) (Policy) College level programs that don’t work even for the students who get there-- Talking About Leaving “Weeding, not cultivating” (Belief) Teacher –centered rather than learner centered (Behavior) Disciplinary culture (“Hyper-competitiveness” of many STEM fields) STEM Education for All: Why Doesn’t this Yet Compute?

18. Framing the Problems Attrition Interest Preparation Hard work STEM Education for All: Why Doesn’t this Yet Compute?

19. Re-framing the Problems Retention Attraction Support Working smart STEM Education for All: Why Doesn’t this Yet Compute?

20. Challenges Messaging matters Money matters Where the learning environment is problematic The quality of the learning experience The culture of STEM Reward structure of the academy STEM Education for All: Why Doesn’t this Yet Compute?

21. Takeaway Lessons Learning from fields with large and consistent increases Looking at interventions within fields that share your challenges Looking at experiments/interventions in computer science STEM Education for All: Why Doesn’t this Yet Compute?

22. Life sciences Near universal course taking in high school High percentage of in-field teachers High percentage of female teachers Compelling topics “Connection to self/community” Critical mass STEM Education for All: Why Doesn’t this Yet Compute?

23. Medicine Removal of “informal’ barriers via legal remedy Perception of openness/fairness  more applications from women More application  more admissions Med schools in MSIs Compelling topics and strong attraction Socially attractive (image and visibility) Strong undergrad advisory infrastructure Clear pathway BUT……. STEM Education for All: Why Doesn’t this Yet Compute?

24. Institutions/Departments/Programs that Stand Out for Success STC’s vs. regular departments Physics vs. Applied Physics at Michigan Kati Haycock’s examples http://www.edtrust.org/dc/presentation/access-to- success-in-america-where-are-we-what-can-we-do- 1 STEM Education for All: Why Doesn’t this Yet Compute?

25. Lessons Learned from Successful Efforts Un-stack the K-12 deck (A’s are C’s; teacher assignment; course availability; remedial focus) Leadership- Student success a priority Tap into institutional culture to achieve student success “Faculty as problem solvers not problems to be solved” Data (disaggregated) for action Make mandatory the things that work STEM Education for All: Why Doesn’t this Yet Compute?

26. Lessons Learned (cont’d) Evaluate programs and make adjustments based on what is learned Develop and monitor retention plans Highlight the clear pathways to success (Rein in choices) Focus on course improvement of introductory and developmental courses Use effective advising models STEM Education for All: Why Doesn’t this Yet Compute?

27. Unpacking the Data Males and females Different URMs Different disabilities Males and females within each URM or disability group STEM Education for All: Why Doesn’t this Yet Compute?

28. % Women Bachelor’s Degrees, disaggregated by race/ethnicity in select “High Performance” STEM Fields, 2010 STEM Education for All: Why Doesn’t this Yet Compute? Agricultural sciences 48% Male 52% Female 42% Male 58% Female Biological sciences Psychology 23% Male 77% Female Source: Calculated from NSF, NCES 2010, Table 5.2

29. % Women Bachelor’s Degrees, disaggregated by race/ethnicity in select “Average Performance STEM Fields, 2010 STEM Education for All: Why Doesn’t this Yet Compute? Earth, atmospheric, and ocean sciences 39% Female 61% Male Source: Calculated from NSF, NCES 2010, Table 5.2 42% Female Mathematics and Statistics 42% Male 58% Male

30. % Women Bachelor’s Degrees, disaggregated by race/ethnicity in select “Average Performance STEM Fields, 2010 STEM Education for All: Why Doesn’t this Yet Compute? Chemistry 49% Female 51% Male Source: Calculated from NSF, NCES 2010, Table 5.2 30% Female Chemical engineering 42% Male 70% Male

31. % Women Bachelor’s Degrees, disaggregated by race/ethnicity in select “Low Performance” STEM Fields, 2010 STEM Education for All: Why Doesn’t this Yet Compute? Physics 80% Male 20% Female 91% Male 9% Female Electrical engineering Mechanical engineering 89% Male 11% Female Source: Calculated from NSF, NCES 2010, Table 5.2

32. % Bachelor’s Degrees, disaggregated by race/ethnicity in “Low Performance” Computer Science, 2010 STEM Education for All: Why Doesn’t this Yet Compute? 18% Female 82% Male Source: Calculated from NSF, NCES 2010, Table 5.2 Computer sciences

33. The Way Forward Single-sex education in low performance fields? (Smith College and Picker Engineering) Critical mass Teacher preparation New curricula (more applications, cultural links, career connection) Experiences and career exploration (e.g., AAAS Entry Point!) “Deliberate inclusion” and questioning absence STEM Education for All: Why Doesn’t this Yet Compute?