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Our intention is to understand the details and complexities of undergraduate
computer science students' experiences, beliefs, and attitudes about computing,
and the nature of the educational institutions responsible for delivering a
computer science education, so that interventions to close the gender gap in
the field are more effective. Nothing about this work has been straight-forward.
Issues of confidence, gender socialization, motivation, culture, play a part
along with the history of computers, curriculum and pedagogy, and the specific
nature of programming itself. We concur with Eccles's observation:
"With increasing data about gender differences in occupational choices at different points of educational process, research models have become increasingly complex, linking achievement-related beliefs, outcomes and goals to interpretive systems such as causal attribution, the input of socializers and gender role beliefs, as well as self-perception and the subjective value of the science/math/engineering task itself" (Eccles, 1994).
We believe that in order to understand the present we must understand
the past. Our primary source of data are interviews with computer science
students themselves. The opening question of our interview is "Can
you tell me the story of you and computers?" We have elicited students'
accounts of their first experiences with computers, who introduced them,
who worked with them, and what did they like to do with the computer. We
are interested in the issues students consider when they decide to major
(or not major) in computer science. We have gathered accounts from each
student about this decision. And then we are interested in what happens
to students once they are studying computer science at the undergraduate
level.
Our study is longitudinal, following students' experiences for two to
four years. The heart of our data is transcripts of semi-structured, open-ended
interviews with students which occur once per semester. Additional data
is gathered through surveys, classroom observations, interviews with faculty,
a small journal writing project, monitoring of electronic communication
and forums.
Participants
There are two sets of participants: computer science majors, and non-computer
science students who are enrolled in an introductory programming course.
To date (April 15, 1998) we have conducted interviews with 51 female and
46 male computer science majors, and with 25 female and 5 male students
who are not computer science majors, for a total of 233 interviews with
127 students.
Our sample consists of the majority of female CS majors at Carnegie Mellon, and a comparable sample of male majors. Among the 51 CS women in our sample are 24 European Americans, 16 international students, 8 Asian Americans and 3 African Americans. Among the 46 men are 28 European Americans, 7 international students, 6 African Americans and 5 Hispanics. We are concerned with the low representation of underrepresented minority groups in the program, which generally tracks the university average of 7-8%. We are also concerned with their experiences: on average, only about half of our African American and Hispanic students persist through graduation. While we have little data to work with, due to the low numbers of minority students, we have been interviewing them as part of our research, with the goal of using the insight gained in future projects.
Interviews
The interviews are semi-structured and open-ended, designed to elicit
students' own experiences rather than their abstract thoughts. Each student
is interviewed once per semester. The opening question of the first interview
is "Can you tell me the story of you and computer science?" It
is here where we hear how much experience they have, who introduced them
to the field, what engaged them, and how they fit computing into the rest
of their lives. At this opening interview we also ask them about why they
decided to major in computer science. Subsequent interviews focus on the
experiences they are having with their course work, peers, faculty, culture
of the field; their focus of interest in computer science; their sense
of belonging (or not) in the field; their aspirations. All interviews are
conducted by either Jane Margolis or Faye Miller and last approximately
one hour.
The interview guides are being continuously added to and revised as
we find that certain issues require further clarification. For example,
we have found it nearly impossible to give relative weight to different
detachment factors; factors frequently shift and appear enmeshed with one
another. Therefore, we have added questions that elicit students' own ranking
of which issues they believe to be the most influential in their attachment
and/or detachment from computer science.
Narrative Summaries
We attempt to keep the participants' stories as whole as possible, to
avoid "context stripping." We attempt to establish a full portrait
of each student. Immediately after each interview, the interviewer writes
a narrative summary of the interview. We rely heavily on narrative summaries
of the interviews to ensure that we consider the interview excerpts in
their full context. We have worked very hard at negotiating the tension
between presenting our data as full portraits and the almost necessary
"fracturing " of the data into discrete elements so that we can
detect patterns across groups and categories (see Maxwell, p. 63).
Interviewing students multiple times allows us the opportunity to check
and re-check our interpretations of each students' experiences. The Carnegie
Mellon student custom of personal web pages gives us another opportunity
to check whether what we conclude from their interviews resonates with
what they publicize about themselves. We also follow the department's electronic
discussion groups, and plan to hold focus groups about our working papers.
Coding
Interviews are audiotaped, transcribed and entered into the computer
software NUDIST. The interview texts are coded for all issues that bear
on students' attachment to and detachment from computer science. Codes
have been developed on the basis of what students discuss, as well as issues
we believe to be salient, based on our prior knowledge and theoretical
hunches. Regularly, we make sweeps through the data, coding for issues
that emerge as most relevant. For example, when we note how connected confidence
and interest are for many female students, we code for this specific link
throughout all of the interviews in our sample. This means that we go into
our data numerous times. We search for patterns among codes, pay attention
to intensity and emphasis of various issues for each student, and use code
frequencies to help establish prevalence and relative importance of mutiple
factors.
We are continuously aware of the challenges posed by this type of data
analysis. How we, as the researchers, hear the interview itself, how we
as listeners and readers read the transcripts, what we decide to hear,
what we regard as important all affect our final analysis. We have found
that if we read the interviews as male vs. female we hear a clear contrasting
sound. But, if we read women's interviews against each other we hear more
complexity, with the stories and motivations of the persisters sounding
different from those who decide to leave.
A Challenge: Longitudinal Instability
Our subjects--being the live creatures they are--change and transform
in response to their own inner workings and to their environment. We have
learned how a student's interview can be so different from her/his preceding
one. For example, those students who decide to transfer out often go through
a period shortly before they decide to leave when they tell us they are
very glad with their decision to have majored, and are planning to stick
with it. They often appear to have resolved some of the key detachment
issues. But, then in the following semester they decide to leave. We are
tracking the cycles that students go through, and our multiple interviews
can help us pinpoint critical decision-making points and factors.
At the 1997 Grace Hopper Celebration of Women in Computing, founder
Anita Borg called for more collaborative research between computer science
and women's studies. Our project turns out to be one of a very few projects
based on this interdisciplinary model. We personally have found this insider/outsider
(who is the insider and who is the outsider?) interdisciplinary collaboration
between computer science and education/women's studies to be intellectually
rewarding and challenging.
We have benefited greatly from many previous research studies of women
in math, science and engineering, and have found the Seymour and Hewitt
book Talking about Leaving (1997), which explores undergraduate
reasons for leaving the sciences, to be especially helpful and provocative
in generating hypotheses. We are constantly reflecting on their findings;
we see ourselves as building on their work, with an opportunity to take
a more in-depth look into one discipline. We are also provoked by the ideas
and writings of Carol Gilligan, Sue Rosser, Jo Sanders, Janet Schofield,
Claude Steele, Sheila Tobias, Sherry Turkle, and other researchers in this
field.
Eccles, J. (1994). Understanding women's educational and occupational
choices. Psychology of Women Quarterly, 18
Maxwell, J. (1996). Qualitative Research Design: An Interactive Approach.
Sage Publications. Thousand Oaks, CA.
Seymour, E. and Hewitt, N. (1997). Talking About Leaving: Why Undergraduates
Leave the Sciences. Westview Press. Boulder, Colorado.