PREP workshop on Emerging Scholars Programs

1. PREP workshop on Emerging Scholars Programs Washington, DC 18 July 2008 Angela Johnson

2. You can download this presentation at: • http://faculty.smcm.edu/acjohnson/PREP/

3. Making the case for a program • Evaluating the first year • Longer-term evaluation • Expanding your program

4. Who I am • MASP • SMESP

5. Making the case • Your ideas? • Think also of the particular people you must convince and how to tailor your arguments to them

6. Making the case • Diversity benefits math and science • Your institution’s track record • National statistics • Equity arguments • “It’s worked before”/“everybody else is doing it

7. Good for math & science • The argument: A more diverse scientific workforce is more creative and energetic; more ideas to draw from • Corporate evidence: More diverse companies have greater profits and market share

8. Good for math & science • The argument: We have a great need for more mathematicians and scientists • Note, though, that ESP programs are about specific kinds of diversity--about increasing the participation of historically under-represented groups

9. Your institution’s record • The argument: Our group of interest hasn’t performed as well in calculus (or in SEM majors) as the norm • And we can do something about this • Possible variables: calc completion rates, calc GPA, % receiving A or B in calc, majoring in math, completing math major, etc.

10. Your institution’s record • Our findings at SMCM: descriptive statistics • “Between 2000 and 2004, 47% of Black, Latino and American Indian students who enrolled in Calculus I did not complete the class; only 27% of White and Asian students did not.” • “Of students who completed the class, 47% of Black, Latino and American Indian students received a B- or above. In contrast, 78% of White and Asian students received a B- or above.”

11. Your institution’s record • At CU Boulder: statistical modeling. After controlling for preparation and financial need, Black, Latino & American Indian students were less likely to graduate in science than White & Asian students (Johnson, 2007a). • See what data your office of institutional research can give you

12. National statistics • The argument: Our group of interest is under-represented among practicing scientists & mathematicians • So let’s encourage more of them • This approach worked at CU Boulder.

13. National statistics • The National Science Foundation has volumes of data • The NSF data can be disaggregated by major, gender, race and US citizenship • Examples: all natural science majors (careful: “Science” includes the social sciences; I had to back them out of the science rates)

14. 2004 college grads Data retrieved from www.nsf.gov/statistics/nsf07308/content.cfm?pub_id=3633&id=2, 1 March 2007, tables 4, 5 & 6

15. 2004 PhD completers Data retrieved from www.nsf.gov/statistics/nsf07308/content.cfm?pub_id=3633&id=2, 1 March 2007, tables 10, 11, & 12

16. 2003 PhD scientists Data retrieved from www.nsf.gov/statistics/wmpd/employ.htm, 1 March 2007, table H-7

17. The good news Data from www.nsf.gov/statistics/wmpd/sex.htm, Tables D-2 & D-3, retrieved 20 Feb 2007

18. The bad news • African American, Latino and American Indian students are less likely to graduate in science than similarly prepared White and Asian students (Huang, Taddese & Walter, 2000, http://www.nsf.gov/statistics/seind04/c2/c2s2.htm#c2s2l2bp3) • At CU Boulder: This pattern persists among declared science majors after controlling for financial need and preparation (Johnson, 2007a)

19. Equity arguments • Which leads to the next argument: Institutional barriers still exist and hinder the advancement of students of color

20. Equity arguments • Making this argument by analogy to women in science: • 2006 report, National Academies • Even after controlling for productivity and the significance of their work, women faculty members are paid less, promoted more slowly, given fewer leadership positions, and awarded fewer honors than their male colleagues. Executive summary, Beyond Bias and Barriers, available at http://www.nap.edu/catalog/11741.html, under “download free”)

21. National Academies report Executive summary, Beyond Bias and Barriers, available at http://www.nap.edu/catalog/11741.html, under “download free”

22. National Academies report Executive summary, Beyond Bias and Barriers, available at http://www.nap.edu/catalog/11741.html, under “download free”

23. Equity arguments • My own work documents the ways in which women of color face bigger obstacles than other science students (Johnson, 2007b; Carlone & Johnson, 2007)

24. Equity arguments • The argument: Certain groups have been historically excluded from math and science • Further: Those groups continue to have less access to high-quality education • Which means that some very able students have not yet been able to demonstrate their potential; with a little extra enrichment, they can do some very fine mathematics

25. Ongoing inequities • Schools are as segregated now as they were in 1967. (Kozol, 2005) • 70% of Black & Latino students attend schools that are essentially segregated • 80% of White students attend schools that are at least 85% white • African American students start kindergarten 1 year behind. By 12th grade they are 4 years behind. (Farkas, 2003)

26. Equity arguments LBJ, 1965, Howard University: “You do not take a person who for years has been hobbled by chains and liberate him, bring him up to the starting line of a race and then say, ‘you're free to compete with all the others,’ and still justly believe that you have been completely fair. Thus it is not enough just to open the gates of opportunity. All our citizens must have the ability to walk through those gates .... We seek not...just equality as a right and a theory but equality as a fact and equality as a result.”

27. Equity arguments • The argument: Racism still exists and affects the choices people make • Therefore, some students with great math potential may have been passed over or discouraged from pursuing math

28. Equity arguments • Résumés with Black-sounding names and excellent credentials received fewer responses than those with White-sounding names and adequate credentials • Bertrand & Mullainathan, 2004 (http://www.povertyactionlab.org/research/papers/bertrand_mullainathan.pdf)

29. Equity arguments • Subconscious bias • Implicit Association test: 71% associate science with men, 9% associate it with women. • To take the test: implicit.harvard.edu/implicit/demo/ • For more info: www.projectimplicit.net/research.php Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., Smith, C. T., Olson, K. R., Chugh, D., Greenwald, A. G., & Banaji, M. R. (2006). Pervasiveness and Correlates of Implicit Attitudes and Stereotypes.. Unpublished manuscript: University of Virginia.

30. “It’s worked before” • Refer to the great evaluations on the bibliography • Make the case for how an ESP program will set your institution apart from its competitors