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

G You can download this presentation at: G G You can download this presentation at: G

G Making the case for a program G Evaluating the first year G Longer-term evaluation G Expanding your program G Making the case for a program G Evaluating the first year G Longer-term evaluation G Expanding your program

Who I am G MASP G SMESP G MASP G SMESP

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

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

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

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

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

Your institution’s record G Our findings at SMCM: descriptive statistics G “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.” G “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.” G Our findings at SMCM: descriptive statistics G “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.” G “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.”

Your institution’s record G 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). G See what data your office of institutional research can give you G 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). G See what data your office of institutional research can give you

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

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

2004 college grads All 2004 college grads 2004 science grads White70.7%67.5% Asian6.4%11.2% Black9.0%8.1% Latino7.7%6.4% American Indian.7% Data retrieved from 1 March 2007, tables 4, 5 & 6

2004 PhD completers All fieldsSciences White77% Asian7.3%10.2% Black7.0%3.3% Latino4.6%4.0% Amer Ind.5%.4% Data retrieved from 1 March 2007, tables 10, 11, & 12

2003 PhD scientists WomenMen White18.4%52.8% Asian5.2%17.2% Black1.4%1.6% Latino.9%1.9% American Indian.1%.3% Data retrieved from 1 March 2007, table H-7

The good news 1997 S&E grad students 2004 S&E grad students All women40%42% Asian2.5%2.9% Black2.7%3.1% Hispanic1.8%2.5% American Indian.2% Data from Tables D-2 & D-3, retrieved 20 Feb 2007www.nsf.gov/statistics/wmpd/sex.htm

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, ) G At CU Boulder: This pattern persists among declared science majors after controlling for financial need and preparation (Johnson, 2007a)  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, ) G At CU Boulder: This pattern persists among declared science majors after controlling for financial need and preparation (Johnson, 2007a)

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

Equity arguments G Making this argument by analogy to women in science: G 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. G Making this argument by analogy to women in science: G 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 under “download free”)

National Academies report Women are not as good in math Girls and boys perform the same in high school now Only a matter of time- -not enough qualified women “Women’s representation decreases with each step up the … hierarchies,” even in fields with lots of women for the past 30 years Executive summary, Beyond Bias and Barriers, available at under “download free”

National Academies report Women faculty are less productive Women’s productivity is now comparable to men’s Women take more time off due to children Women take more time off early in their careers; over a lifetime, men take more sick leave than women Executive summary, Beyond Bias and Barriers, available at under “download free”

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

Equity arguments G The argument: Certain groups have been historically excluded from math and science G Further: Those groups continue to have less access to high-quality education G 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 G The argument: Certain groups have been historically excluded from math and science G Further: Those groups continue to have less access to high-quality education G 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

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

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.”

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

Equity arguments G Résumés with Black-sounding names and excellent credentials received fewer responses than those with White-sounding names and adequate credentials G Bertrand & Mullainathan, 2004 ( G Résumés with Black-sounding names and excellent credentials received fewer responses than those with White-sounding names and adequate credentials G Bertrand & Mullainathan, 2004 (

Equity arguments G Subconscious bias G Implicit Association test: 71% associate science with men, 9% associate it with women. G To take the test: implicit.harvard.edu/implicit/demo/ implicit.harvard.edu/implicit/demo/ G For more info: 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. G Subconscious bias G Implicit Association test: 71% associate science with men, 9% associate it with women. G To take the test: implicit.harvard.edu/implicit/demo/ implicit.harvard.edu/implicit/demo/ G For more info: 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.

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