Gender Equity in Computing Rita M. Powell Department Manager

Gender Equity in Computing
Rita M. Powell
Department Manager
Dept. of Computer & Information Science
Statistics on Women’s Participation in
Computer Science:
20% of undergraduates in selective computer science
programs are women.
 Women held only 24% of degreed IT positions.
 Technology jobs will increase by 75% between 2000 and
2010, accounting for 15 percent of all new jobs in the
American economy.
 In 1998, women earned only 27% of the degrees awarded
in Computer Science.
 Women’s retention in undergraduate programs of
Computer Science is only about 42% as compared to 70%
for men.
Why and What can we do?
What the research tells us:
Influencing Factors
1. Family Environment and Support
2. Student behavior factors such as attitudes,
aspirations, and academic preparation.
3. School/institution factors such as precollege
curriculum and instruction and postsecondary
special programs in recruitment, retention and
financial aid.
Family Environment and
Parent’s educational attainment and
Student Behavior—Attitudes and
Gender differences
Low self-confidence
Attitudes regarding quantitative
Relative to men,
– women may be more sensitive to social
– value more human aspects of the environment.
– Women dislike highly abstract nature of S&E
– Women tend to dislike strong competitive
environment of S&E depts.
Gender Roles
Gender roles—prioritizing career vs.
marriage/family Research mixed here. Some
researchers found that women with strong career
commitments gravitate towanrd S&E.
 Unique psychological difficulty of minority
students—Over-confidence and poor preparation
 These attitudes become apparent during middle
school and are held by college women.
Academic Preparation
Gender gap in science related achievement not as
great as gender differences in science related
attitudes and activities.
 Girls and underrepresented minorities found to
have taken fewer advanced courses in math in
high school than males and white and Asian peers.
 Girls that major in computer science are often the
products of and excelled in schools with strong
math curricula.
School/Institution Factors
Precollege Education
– Intensive curricula and excellent instruction in high
school mathematics coupled with high expectations
– Teachers and Counselors
Counselors’ encouragement in choice of S&E majors.
Quality of math and science teachers.
Teachers’ expectations of girls vs. boys
Teachers’ interaction with girls vs. boys
Postsecondary Institutions
Small liberal arts colleges with an
apprenticeship model of education do
 Academic and Social climates of the
institution: Contents and approach to S&E
education are inherently disadvantageous to
women and underrepresented minorities.
Valuing personal success VS. Valuing people and
 Over-emphasis on grades at the expense of other
assessment methods
 Need to match special programs for recruitment
and retention with effective program organization
to support admitted students.
 Need to offer Enrichment programs, not remedial
My study: freshmen
women’s persistence in
computer science at Penn
Women’s issues that impact their success
in the first year of the major:
Lack of a community
 Perceived lack of prior experience in computer science
Lack of confidence in their ability to learn computer
 Distaste for the competitive, male environment termed
“CSish attitudes,” impeded cooperative and collaborative
learning and formation of community within the student
 Testing Anxiety which develops from Stereotype Threat.
 Poor or mediocre grades.
 Fear of not being passionate enough about computer
Remedies: What helps? What works to
encourage women’s persistenc
high school teachers’ encouragement
Parental encouragement and support
Build community within the school and the
department through shared activities and experiences.
Increase collaborative learning opportunities
Increase faculty involvement
Remedies: What helps? What works to
encourage women’s persistenc (cont.)
Show students what they can do with computer science in
addition to programming:
Artificial Intelligence
Cognitive Science applications
Natural Language Processing
Computer animation
Business applications
Network security
Internet application
Computer game development
– Computer vision