Download - Gender STEM and Women´s education, Linda Sax
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Gender Equity, STEM, and Women’s Education
Professor Linda J. Sax
UCLA
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What Environments are Best for Female Students?
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Overview of Current Research Projects
The Gender Gap in STEM
Women in Computer Science
The Role of Single-sex Education
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The Gender Gap in U.S. College Enrollments
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
Women
Men
Source: National Center for Education Statistics, 2012
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Women Overrepresented Across All Fields, but
Underrepresented in STEM
All Bachelor's Degree Recipients STEM Bachelor's Degree Recipients0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
5735
4365
Proportions of Bachelor’s Degree Recipients in the U.S., by Gender
Women Men Source: National Center for Education Statistics, Digest of Education Statistics, 2011
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Women’s Underrepresentation in STEM:
Why Does it Matter?
Women’s economic independence
US global competitiveness
Inclusion of diverse perspectives in STEM
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Explanations for the Gender Gap in STEM
Educational Settings
Forces Beyond the Classroom
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Educational Settings
Women begin to opt out of STEM courses in middle and high school Women underrepresented in AP courses in STEM
(Calculus, Physics, Chemistry, Computer Science)
Unwelcome climate in many college STEM majors Large lecture halls, grading on a curve Underrepresentation of women = Less opportunity for
female friendship groups in STEM
Teachers/Faculty More traditional teaching practices (emphasis on lecturing,
not student-centered methods) Faculty seen as intimidating (more impactful for female
students) Lack of female role models and mentors in STEM
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Forces Beyond the Classroom
Sense of belonging in STEM Science perceived as masculine domain by students and
parents Science careers perceived as competitive, unwelcoming
and difficult to balance work-family Societal benefits matter, but not clearly understood
Women’s Lower Self-confidence (% rating “above average” or “highest 10%” in 2011):
Computer abilities (47.4% of men, 30.3% of women) Math abilities (55.6% of men, 36.1% of women)
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Enduring Gender Gap in Self-Rated Mathematical Ability
(% Above Average or Highest 10%)
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
-10%
0%
10%
20%
30%
40%
50%
60%Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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How Has the Gender Gap in STEM Changed Over Time?
1976 1986 1996 2006 20110
5
10
15
20
25
30
35
40
31.929.1
32.6
28.0
36.6
12.711.0
15.814.4
20.0
Proportion of Students Intending to Major in STEM, by Gender
Men Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Gender Gap Narrows…Then Widens
1976 1986 1996 2006 2011
-25
-20
-15
-10
-5
0
-19.2-18.1
-16.8
-13.6
-16.6
Difference in Men’s and Women’s Intention to Major in STEM
Proportion of Women-Men
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Need to Consider Differences Across STEM Fields
Computer
Science
Biological
Sciences
Math
Engineering
Physical
Sciences
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Women’s Relative Representation in STEM Varies by Field
Engineering Computer Science
Physical Sciences
Mathematics/Statistics
Biological Sciences
0%10%20%30%40%50%60%70%80%90%
100%
17 1841 43
58
83 8259 57
42
Proportions of Bachelor’s Degree Recipients, by Gender
Women Men
Source: National Center for Education Statistics, Digest of Education Statistics, 2011
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Plans to Major in Biological Sciences, by Gender (1971-2011)
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Plans to Major in Math/Statistics, by Gender
(1971-2011)19
7119
7219
7319
7419
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
0820
0920
1020
11
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Plans to Major in Physical Sciences, by Gender (1971-2011)
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Plans to Major in Engineering, by Gender (1971-2011)
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0%
5%
10%
15%
20%
25%
Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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Proportion of Entering Students Who Plan to Major in Computer Science, by Gender
(1971-2011)19
7119
7219
7319
7419
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
0820
0920
1020
11
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
Men
Women
Source: Cooperative Institutional Research Program Freshman Survey, Higher Education Research Institute, UCLA
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In order to attract more women to engineering and computer science, we need to better understand women who majors in these fields
How do they differ from men in those majors?
How do they differ from women in other STEM fields?
How have they changed over the past four decades?
How Can the Research Help?
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Data Source: CIRP Freshman Survey Over 8 million students entering over 1,000
baccalaureate institutions
Focuses on 5 STEM fields: Biological sciences, Computer Science, Engineering, Math/statistics, Physical Sciences
Analyzes female and male STEM majors over the past 40 years (1971-2011)
Results will be available in 2015
NSF-Funded Research(HRD #1135727)
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Women Graduates of Single-Sex High Schools
and STEM Outcomes
Research supported by the National Coalition of Girls’ Schools
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Growth of Single-Sex Schooling in the U.S.
Single-sex schools viewed by many as possible antidote to gender inequities in co-ed settings How Schools Shortchange Girls (AAUW, 1992) Failing at Fairness: How Schools Cheat Girls (Sadker and
Sadker, 1994) Private single-sex schooling grows in the 1990s In 2006, U.S. Department of Education authorizes single-sex
classes in public schools Between 2007 and 2010, more than 1,000 of the nation’s
98,000 public secondary schools report having single-sex academic classes
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Single-Sex Education and STEM
Can single-sex education encourage more young women to consider STEM fields?
Does an all-female learning environment enable young women to be more confident in the classroom? To take more advanced classes? To consider a STEM career?
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Research Questions
1. How do female graduates of private single-sex and coeducational high schools differ from each other at the point of college entry? Career aspirations and major field Academic and social self-confidence Reasons for attending college Expectations for college Life goals Attitudes on political and social issues STEM Outcomes (e.g., Math confidence, STEM career aspirations)
2. What are the “net effects” of single-sex secondary schooling after controlling for students’ demographic background and other high school characteristics?
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Data Sources
2005 “Freshman Survey” conducted by UCLA’s Higher Education Research Institute
Four-page questionnaire administered at beginning of first year in college
Measures students’ academic and family backgrounds, reasons for college, goals, major and career orientations, etc.
Chief comparison groups: Women from private single-sex high schools (n=825 students) Women from private coeducational high schools (n=5,587 students) Women from public coeducational high schools (over 300,000)
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STEM-Related Outcomes
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Mean SAT Scores
Independent Single-Sex
IndependentCoed
Public
SAT Math 650** 628 586
SAT Verbal 660** 639 587
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Self-rating: Mathematical Ability(% above average or highest 10%)
48
37 39
0
20
40
60
80
100
Independent Single-Sex** Independent Co-ed Women from Public Schools
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Self-rating: Computer Skills (% above average or highest 10%)
36
26 28
0
20
40
60
80
100
Independent Single-Sex** Independent Co-ed Women from Public Schools
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Engineering Career Aspirations
4.4
1.4
2.6
0
2
4
6
8
10
Independent Single-Sex** Independent Co-ed Women from Public Schools
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Research Question #2
What are the “net effects” of single-sex secondary schooling after controlling for students’ demographic background and
other high school characteristics?
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“Net Effects” of Single Sex Education
Differences between single-sex and coeducational graduates after accounting for:
Students demographic backgrounds, including: Race/ethnicity Family income Parental Education
High school characteristics, including: Enrollment Selectivity Curriculum Location
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“Net Effects” of Single-Sex Education
Single-sex education positively predicts…
Academic engagement (especially studying) Interest in politics Propensity to participate in student government in college Self-ratings of math ability Self-ratings of computer skills Interest in pursuing engineering careers
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Thank you!
The Full Report is available at:
http://www.ncgs.org/Pdfs/FINAL-REPORT.pdf