Senior Thesis - Unpublished

A Qualitative Study of Motivation, Experience, and Feelings of College Women in
Pursuit of a Degree in Computer Science

Al Youngblood
SOCI 3309, Qualitative Methods
Professor Patti Giuffre
April 28, 2003



Table of Contents

  1. Introduction . . . . . . . . 3

  1. Review of Literature . . . . . . . 5

  1. Data and Methods . . . . . . . . 13

  1. Findings . . . . . . . . . 17

  1. Discussion and Conclusion . . . . . . 22

  1. References . . . . . . . . . 24

  1. Appendix A: . . . . . . . . 28

 Figure 1. Life stage factors affecting women in computing
 Figure 2. Factors Influencing Participation of Women in CS Course

  1. Appendix B: Interview Questions . . . . . 29

  1. Appendix C: Consent Form . . . . . . 30

  1. Appendix D: Email Correspondence . . . . . 31

  1. Appendix E: Transcripts . . . . . . . 32

Interview #713: 	          White female, 22 years old
Interview #229: 	          White female, 23 years old
Interview #523: 	          White female, 37 years old
Interview #263: 	          White female, 39 years old



Introduction

Ever since its humble beginnings as a programmable steam calculator, computer technology has been fundamentally advanced by female achievement. Most of these women, unfortunately, never have been featured alongside their more famous male colleagues and are relatively unknown (Gürer 1995). Women still lack professional recognition and the prospect for advancement as compared with male counterparts in a broad range of technology and computing fields. (Teague 2000).

Computer programming, in particular, has been a field wherein women have demonstrated a level of achievement on par with men. Most early computer programmers were women—so-called “calculators” or “computers”—who believed they were accorded equal respect with men because, as one gender stereotype then held: “Programming requires lots of patience, persistence and a capacity detail and those are traits many girls have.” (Gürer 1995:47). After WWII, the growing field of computing sciences experienced a shortage of professionals that, at the time seemed to be a radical proposition, could be relieved by recruiting women in place of men (Rossi 1965). Rossi’s famous declaration of the “social and psychological influences” that restrict college-educated women from science career advancement has been noted as the conscious birth of a “women in science” movement, and is seen as the beginning of a new understanding within the field (Etzkowitz et. al 1994; Rayman & Brett 1995).

Today, a wide range of research literature on gender and computing, most of which has been entirely quantitative (Kay 1992), ignores the critical importance of a qualitative methodology that offers a “thick description” of the embedded social processes of women who negotiate symbolic spaces in computing (see, generally, Berg 2001). In fact, some researchers have begun to question gender-computer theorization, in general, for its lack of qualitative 

sophistication and frequent overgeneralization, both of which ordinarily frame the underrepresentation of women in computing as a broad “problem,” rather than as a localized dynamic worthy of detailed case studies in account of a woman’s agency (Clegg 2001; Henwood 1998). A “deficit model” of women lacking some stable ingredient for equity with men has been a common assumption in gender-computing studies; however, such a formulation cannot account for simple gendered experience, motivation, or feelings that are too often reduced by statistics and inferences. A qualitatively-grounded study into motivations, feelings, and experience must rely on some previous quantitative conclusions and theory for comparison that attempts to “bridge a balance,” instead of allowing such a study to be mere “story telling” (Kay 1992:163).

Only one published, peer-reviewed article, suggesting a “deficit” in undergraduate science education (Lorna et. al 1998), has ever attempted to qualitatively examine the motivation, experiences, and feelings of university women in science through in-depth interviews (see, broadly, Dryburgh 2000). No study has ever specifically done so within the field of undergraduate computer science, though several topical articles have appeared in trade publications (e.g., Teague 2000).

With these considerations in mind, the author conducted in-depth interviews with five upper-division women to uncover the motivations, experiences, and feelings with respect to three broad research goals related to computer science (“CS”):

As several gender-computing researchers have noted, this study’s qualitative posture should better enable it to move from “identifying” issues towards “understanding” them (Kay 1992:163; Lorna et. al 1998; Charlton & Birkett 1999).

Review of Literature

Without question, there exists a monumental amount of research confirming that the distinction of gender accounts for, or is related to, differential access, process, and outcomes relative to men within economic, political, and social institutions (Shakeshaft 1995; Lorna, et al. 1998; Rayman & Brett 1995; Young 2000; Etzkowitz et al. 1994). The study of gender encompasses an impressive swath of sub-disciplines, among which education and the schooling process have been studied extensively as a key socializing process (Eckes & Trauner 2000). Because gender relations at the post-secondary level hold broader implications for more fundamental issues in inequality, stratification, and discrimination— that is, they often a stepping stone for professional workplace and institutional development—gender relations should merit the attention of sociological researchers (Jacobs 1996).

Some researchers have tried to bring together a broad range of considerations when trying to understand technology, gender, and education. According to Kay (1992:161-2), a methodological and theoretical paradigm shift towards understanding the affect of gender difference in “attitudes, aptitude, and use” would aim towards studying human and computer interaction by: (1) collecting qualitative data; (2) formulation of a context-based, as opposed to constructive, philosophy for shifting data from contradiction; (3) examination of more fundamental factors and concepts; and (4) use of a developmental orientation to study emergent behavior. All of these shifts are subsets of broader research dialectics: (a) qualitative and quantitative methods, (b) construct and contextual theory, (c) general learning and specific task, and (d) static and dynamic approaches. After all is said and done, however, “[g]ender is but one piece of the human-interaction puzzle” (Kay 1992:167).

Other literature has tended to ground their approaches within a more robust framework in order to study gender, computer science, and education. Lorna et al. (1998) identified three major research orientations for studying issues of gender, science, and college women: (1) ability and academic preparation; (2) Social-psychological factors; and (3) Culture-structural environments. The Dryburgh (2000) classification of research on women and computing encompassed all the Lorna et al. (1998) orientations, but added the larger considerations of: (1) “Educational Stage,” which used cohort age to segregate into “Elementary School,” “Secondary School,” and “Post-Secondary School” categories; and (2) “Comprehensiveness,” which considers whether a study was “Comprehensive” (i.e., used random sampling design for generalizability) or “Non-comprehensive” (i.e., pilot reports, qualitative studies, etc. that lack broader application). Appendix A, Figure 1, shows the detailed research considerations.

Within this study, the Lorna et al (1998) and Dryburgh (2000) models are used freely to identify, interpret, and analyze collected data. A careful ear is given to the broader theoretical implications advocated by Kay (1992). As a reference mode, two informative Dryburgh (2000) figures are included in Appendix A to show linkages between women and computing. Furthermore, because social-psychological theories are essential to “understanding” the scope of the gender-computing issue (Kay 1992; Lorna et al. 1998; Sacks et al. 1993), they will be utilized almost exclusively in this study, in line with most recent studies of post-secondary college women in science (Dryburgh 2000).

  1. Social Psychological Orientations

    1. Self-Efficacy

Bandura’s social-psychological concept of “self-efficacy” is used extensively in literature on gender and computing (Zhang & Espinoza 1997; Cassidy & Eachus 2002).

Generally conceived, self-efficacy is the sum total of judgments an individual makes with respect to their own performance of a certain task in any situation, or a belief in one’s ability to do something. Gecas (1989:294) distinguishes between “efficacy expectation,” that one can personally do something, versus “outcome expectation,” that something gets done within one’s environment, to relate the self to the broader social environment. Futility and frustration arise when an action results in no action being executed.

Self-efficacy is positively correlated to increased experience, frequency, and success with computers, and undergraduate men have higher self-efficacy over undergraduate women, independent of beliefs as to the value of computer use (Cassidy & Eachus 2002:134-5; Zhang & Espinoza 1997). Self-efficacy is directly related to positive computer attitudes by women and persistence in their tasks, and a lowered self-efficacy level may cause a woman to give up achieving her goal (Zhang & Espinoza 1997; Levy et al. 1991). Indeed, a “strong link” exists between gender role orientation and occupational self-efficacy (Eckes & Trautner 2000:382).

The ratio of self-efficacy for men and women is less at lower levels of computer specialization, and increases at more advanced and differentiated tasks, like mainframe management and software management; but these differences can be cured by training (Cassidy & Eachus 2002). Remedial computer “catch-up” courses do not consistently increase self-efficacy in women, probably because the type of experience is important, rather than mere computer experience per se (Charlton & Birkett 1999). Achievement in CS may be the greatest means of fostering a woman’s CS ability (Rayman & Brett 1995).

    1. Self-Esteem, Confidence, and Anxiety

Self-esteem, a key motivational aspect of the entire “consistent” self that relates to “positive evaluation” (Elliot 1996:207-8) and is found in many “domains of competence” rather than a unified construct (Richman & Shaffer 2000:190), has been correlated to positive computer attitudes by girls (Shakeshaft 1995) and by women (Lorna et al. 1998). Women have lower math and science “confidence,” a psychological component of self-esteem (Levy et al. 1991), even though they may perform equally or greater than men, and this difference is correlated to lower science participation from childhood to adulthood (Lorna et al. 1998; Teague 2000). Most women do not continue to upper-division CS classes because their interest never has gone beyond introductory courses often required with other college majors (Sacks et al. 1993).

Some organizational researchers have identified self-esteem as a primary “global construct” that holds larger goal setting implications because those individuals with higher self-esteem have more confidence and set higher goals (Levy et al. 1991). Goal setting is essential in differential outcomes within science fields. In addition, gender role expectations that “science is for men” may work to deny women the ability to accurately estimate their true competency (Lorna et al. 1998). Some women may perceive that “all women” can be competent, yet still deny their own specialized skills—the so-called “We can, but I can’t” paradox (Jennings & Onwuegbuie 2001).

When bridging self-esteem with goal setting, some important conclusions from the literature can be shown. Elliot (1986) posits that low self-esteem is a correlated with anxiety, an low computer confidence translates into heightened computer anxiety for college-age women (Cassidy & Eachus 2002). Since goals are structurally-related to gender role socialization (Eckes & Trautner 2000), one researcher has implied that gendered goals may delimit a woman’s potential self-esteem and, in turn, increase the computer anxiety that a woman may experience when entering CS (Brosnan 1998).

    1. Attitudes and Engagement

Interest in computers strongly motivates entrance by women into the CS field (Dryburgh 2000). Direct or indirect experience with a computer is correlated with computer interest and positive attitudes by both boys and girls; however, boys may have higher participation rates and ultimately more favorable attitudes (Sacks et al. 1993). Some researchers have contradicted the notion that female attitudes of computers are entirely negative by noting that age, and not necessarily gender, are the principal factors. Indeed, there may be no correlation between all women and all of computing (Jennings & Onwuegbuzie 2001).

Attitudinal research suggesting a lessened female computer engagement— “a behavioral construct indicating a high degree of computing activity” (Charlton & Burkett 1999:238)—has been confirmed by numerous studies. Sadly, since student attitude is correlated with willingness to obtain computer skills (Zhang & Espinoza 1997), a negative female attitude as to her computer skills may never lead to improvement because she will not engage her potential. Whatever the cause may be for computer attitudes, programming in particular has seen the most marked contrast in preference with women marking programming as unfavorable (Sacks et al. 1993; Young 2000).

Brosnan (1998) notes that the “fun” of computer games and tasks distinguishes boys from girls who often under perform on such tasks. Women have found computers to be more of a tool, rather than an end in and of itself (Charlton & Birkett 1999). Put another way, women tend to see the computer as a tool to engage the world.

    1. Persistence, Diligence, and Competition

Differences in college gender competition patterns may be indicative of greater societal expectations and different performance strategies—e.g., women are less “task-specific” than men—with women ultimately holding lower expectations of success (Silvestri 1990:364,366). Studies on children have shown different math test-taking strategies. Boys guess and estimate answers, while girls work out the exact answer (Shakeshaft 2001). Women perceive that men perform better in competitive situations, partially due to lowered expectations of success, even though that is not the case, whether alone or in competition, and perhaps, there is a latent fear of success (House 1974). A positive psychosocial pattern of self-esteem that develops within an earlier competitive domain (i.e., sports) may yield college academic competence and other positive success factors for undergraduate women (Richman & Shaffer 2000).

Some researchers have commented that competition and innovation are stifled when girls in science are not allowed to fail. Then, “helping is hurting” (Shakeshaft 1995), and parents and teachers “kill with kindness” (Young 2000). For college women, the culprit is manifested psychosocially. The persistence and continued success of women in college and in industry is correlated to self-esteem, self-efficacy, and “resiliency,” although women are likelier than men to portray their academic success as a result of external “luck” rather than sheer ability or competency (Rayman & Brett 1995:389). Of course, such behavior by women was put in place long before they arrived in college and is correlated to structural inequities in place to encourage those expectations and outcomes (Jacobs 1996).

  1. Structural and Cultural Environments

Traditional quantitative explanations of gender difference with respect to women and computing have looked at more structural considerations for their conclusions. These are of greater importance in post-secondary schooling (Dryburgh 2000).

    1. “Chilly Climate” and “Critical Mass”

A masculinized cultural environment—better known within the literature as a “chilly climate” for women that degrades self-esteem and aspiration—has also been an important obstacle through which women must negotiate. Sexist commentary, little or no other peers or role models, jokes about female capabilities, lack of attention, lowered perceptions of a woman’s capabilities, are some patterns that exist within chilly climates (Lorna et al. 1998). This literature, while compelling, cannot be taken at face value. Studies have shown that persistence is not entirely related to these environmental conditions (Lorna et al. 1998; Canada & Pringle 1995).

The “women in science” movement has appropriated the term “Pipeline” to signify the growing supply of CS women into a reservoir of potential faculty mentors and role models, all of which will yield a future “critical mass.” Such symbolic affirmations are not unusual in technology cultures to explain and anticipate a social change (Henwood 1996). As Etzkowski et al. (1994:53) explain, developing a “critical mass” means that women would begin to see more of their own, and consider themselves as “normal,” and that male colleagues would have less incentive to use their institutional power and privilege against the female minority group. The chilly climate would then begin to defrost and invite more women (Lorna et al. 1998).

    1. Classroom Environment

Canada & Pringle (1995) describe classroom interactions using the gender prescribed rules of “agency” and “communion.” Agency and communion are concepts derived from David Bakan’s notion that agency orientation characterizes autonomous goal orientations, such as that with leadership behaviors, and that communion orientation characterizes behaviors of service to others (Strage 1999:300). Mixed group interaction patterns of children hold different outcomes for girls and boys—the “enabling” (i.e., less-defined strategies) of girls are typically dominated by the “restrictive” interaction of boys and, as a consequence, boys assume leadership position and privilege within the classroom setting (Canada & Pringle 1995:185). Agency and communion orientations within the technical classroom setting are important and are evident within mixed-sex college environments (Strage 1999:310).

Charlton & Birkett (1999) suggest that educators use a schizoid personality type, which involves more reflective intellectual thinking and introversion of science (so called “tough-poise”) in developing explanations for gendered differences in computer engagement and classroom settings, and that the converse of this dynamic, called “emotion,” should be left for arts and humanities students. Shakeshaft (2001:77) is concerned that such systems will only perpetuate gendered teacher-student interaction patterns in pre-secondary levels that promote boys who are “smart” and girls who are merely “hard working” and “lucky.”

Some studies have shown a connection between single-sex classrooms with math and science advancement for young women (Canada & Pringle 1995) and college-women (Rayman & Brett 1995; Dryburgh 2000). Although girls can perform equally with boys in mixed-sex environments, the time and cost involved changing an institutional culture is just too great (Shakeshaft 2001).

    1. Stereotypes and Gender

By all accounts, computer science and technology is viewed as a “masculine” field (Lorna et al. 1998; Dryburgh 2000). Girls are more likely to consider themselves “outsiders” (Shakeshaft 2001), less likely to “claim computers as their own group” (Young 2000), and less likely to consider it fun or interesting (Teague 2000). A common stereotype involves the imagined irreconcilability between being a “scientist” and being a “mother” (Lorna et al. 1998).

Even when women do enter the CS field, the disjunction between “psychological gender” and gender task appropriateness causes anguish and anxiety—or, to the extent that being in CS means being masculine, discordant social-psychological anxiety will arise in women of low masculinity (Brosnan 1998). Being related to expectancy issues of self-confidence, task’s appropriateness arises in task performance (Silvestri 1990).

    1. Material Advantage

Men and boys have tended to monopolize computing resources at home and at school, leaving women to find other avenues for using computers, if at all (Young 2000; Charlton & Birkett 1999). Computer ownership is positively correlated to increased computer experience (Lorna et al. 1988). When examining more advanced CS and programming courses and in contrast to non-specialized studies showing that male ownership of computers seemed to confer an experiential advantage, Charlton & Birkett (1999) found that a woman’s lack of computer engagement was not related to ownership, but rather gender differences in purchasing—all of which did not significantly affect course performance.

Data and Methods

A voluntary sample was selected from five upper-division, undergraduate women majoring in computer science (CS) at a large regional university in the Southwest using both snowball and purposive sampling. The sample was selected during a 4-week period in the Spring semester of 2003. The author and the participants did not know each other personally, nor had the author taken any courses with the participants.

  1. Data Collection

With the assistance of an interested female CS faculty member, who did not personally know the subjects, an email correspondence was twice mailed to six potential candidates asking for volunteers to participate in a confidential one-hour tape-recorded interview (See Appendix D). The email initially used the faculty member’s name to utilize her positional authority, but on subsequent communication the author’s name was used in association with the Department of Sociology at the university. One criterion was being an upper-division CS major. Another criterion for purposive selection was that the candidate not be foreign-born, which was established based upon a cursory evaluation of the surname on record while at the university. The CS faculty member noted this would not be a problem as most undergraduates in the CS department were “native-born.” Age, race, ethnicity, and socioeconomic status were not considered as criteria.

Four women elected to participate after a series of phone calls and follow-up. One rescinded her offer to participate after missing an appointment. Independently, another candidate, and her CS lab partner, elected to participate upon the invited suggestion of a CS lab coordinator. In total, five women—all white women: two in their early 20s, two in their late 30s, and one in her late 50s—were interviewed in a semi-structured format during the first week of April 2003 at a university dining and meeting space. No designated location was preferred, other than it allow for privacy.

After meeting, a brief informal conversation was initiated to “break the ice” (Berg 2001). For all respondents, the author initially offered to purchase food items (e.g., bottled water, coffee, a smoothie, and a full chicken sandwich meal) telling the subjects that the Department of Sociology was gracious in its funding for the author’s project, to which four women happily obliged. However, participation in the study was never contingent upon receiving a food item.

Interviewer and interviewee sat directly opposite one another, with a blue tape-recorder placed in plain view alongside the author’s other collected papers. The author spoke in a calm, interested voice, but would make expressive gestures if he believed a response deserved some elaboration or for extension of the expression. Berg (2001) advises that once a point is open that is should be pursued. During the interview, minor notes were taken on a small scratch pad, though the author did occasionally pretend to write down information in plain view of the participant to elicit more detailed responses.

The author told interviewees that he was a CS major with knowledge in the programming languages of JAVA, C++, UNIX, and BASIC so any references to technical computing aspects could be shared. The author purposefully used the term “woman,” in reference to any female adult, except when referring to “girlfriends” (the respondent’s female friends), as opposed to “girl,” which all respondents chose to use.

The author employed a “longitudinal” biographical method (Merriam 1998), where a brief background inventory followed directly towards post-secondary schooling. Open-ended questions and “question clusters” were used. A tape transcript was produced to accurately reflect all recorded conversations (See Appendix E). Each respondent was given an arbitrary number for assignment to their cassette tape. On the transcript, references to “flipping the tape” or “took a break” indicate that the conversation flow was halted.

  1. Structure and Design

The study is framed as a “basic or generic qualitative study,” which shares a particular set of characteristics:

Merriam (1998:11).

Only four interviews were considered in the study. One woman’s responses were discarded because she did not represent the population of female undergraduate CS majors, as she already obtained two bachlor’s and one master’s degree. Interview questions were geared toward addressing three central research questions dealing with motivation, experience, and self-perception/feelings (See Appendix B).

  1. Analysis

Three colors, each signifying a major research orientation for studying post-secondary gender segregation in Lorna et al. (1998), were used for data mining within the typed transcripts: Yellow for “Ability and Attrition,” Pink for “Social Psychology,” and Green for “Culture and Structure” (See, generally, Merriam 1998; Berg 2001). Because interviews were structured chronologically, the Dryburgh (2000) criticisms of generality could be taken into account.

Yellow is broadly correlated to how the women generally acknowledged their performance in schooling, preparation and achievement, completion of math and science courses, GPA, SAT scores, and their interests in specific types of subjects. Pink is broadly correlated to motivations, goals, self-efficacy, self-esteem, attitudes, aptitude, expectations, identity, confidence, persistence, competition, conformity, femininity, and valuation. Green is broadly correlated to cultural or structural components like number of males versus females in a class, role models and advising, support networks, public or private schooling, lab settings, teacher-student interaction, discrimination, and some demographic information like family class, income, parent’s education level. The Yellow, Pink, Green (YPG) coding scheme was not only crucial to simplifying often overlapping and contradictory studies, but also to marking theoretical frameworks for data interpretation and correlation to existing literature.

  1. Ethical Considerations

Standard ethical considerations of the university institutional review board (IRB) were followed. Before commencing, the author read aloud a prepared “Consent Form,” which was taken from the study advisor’s own suggested form (See Appendix C) Interviewees were required to sign two copies, one of which they kept. One woman verbally gave her consent. All cassette tapes from interview conversations are in the physical possession of the author. Only the study advisor and the author have access to transcript materials.

Findings

  1. Participant Profile

The respondents were full-time students, currently enrolled in upper-division classes, majoring in computer science (CS) and minoring in math. A career in science first appealed to the women during early to middle adolescence, when all of them were involved in extra-curricular activities such as music or sports (See, generally, Richman & Schaffer 2000). Like the Lorna et al. (1998) respondents, interviewees hesitated in fully expressing their ability. The prospect of turning their long, hard-earned CS hours into a means of self-support, one that would secure them away from menial, minimum-wage jobs, was a primary consideration for completing their program. They appreciated the career and economic advancement that would follow their completing a CS degree.

All women acknowledged the “masculinity” or male-domination within CS field, even though the interviewer purposefully did not define his terms in that regard. Women wished there were more role models, and for some, a better advisory network for expressing their course selection and communicating their concerns. All agreed with the basic instructional premise of “women can [achieve], but I may not” (Jennings & Onwuegbuie 2001:369). Relative to a more or less tacit father, a strong mother was instrumental for advice and to provide support and encouragement throughout earlier elementary and secondary schooling. Expectations were kept high enough for advancement to the next educational stage. Social class and income played a part in assuring status attainment would perpetuate “unquestioned” expectations for attending college for three of the women (Rayman & Brett 1995)—that is, these women always expected to attend college.

Three respondents at some point in their early schooling, at home, or work, were introduced to computers by a male figure, most commonly their fathers or brothers. Through successive interaction in a variety of contexts—i.e., through video games, simple BASIC programming, or word processing—these women gained an appreciation and interest for a computer’s usefulness in solving problems posed to it, which was distinguished from its hardware components. None of the participants particularly liked hardware components.

The older two women first experienced computers late in high school. The oldest study interviewee experienced high levels of self-doubt and anxiety, partially due to her perceived poor test-taking skills. Another woman in her peer group did not have this anxiety, though she utilized a mature masculinized relationship with her father (See, e,g., Lorna 1998). Both younger women did not have anxiety when interacting with CS material, as was the case with the older cohorts who never had ready access to computers during their formative academic years. This is consistent with the findings of Jennings & Onwuegbuzie (2001). The most anxious respondent perceived herself to be the least confident in her studies.

  1. I’ll Compete, But Within “Limits”

During interviews, competition was measured to the extend that it was bounded by some external or internal lowered expectation, and not with respect to any “game,” goal, or purpose. This is in line with House (1975) who suggested that women avoid competition whenever possible within a framework of conflict:

Q: Were you competitive when you were younger?

A: Not to a bad extent. Not to where it gets in the way of being competitive, and

It’s all about winning. […]


Q: So balance is important?


A: Yeah. Everyone needs to realize that most people are not going to be #1.


(Interview #263).


Q: Do you consider yourself as a competitive person?


A: In grades, I am. […]


(Interview #713).


[…]


Q: Why weren’t you mad?


A: […] I was just trying to do good (sic) on the test. I wasn’t trying to compete with

anybody.


(Interview #229).

The significance of this finding rests in the fact that women must sometimes outperform their male colleagues in order to reaffirm their own self-conscious ability (Charlton & Birkett 1999). Gender role socialization anticipates passive female behaviors (Eckes & Trauter 2000), which eventually feed upon themselves at higher levels of schooling (See, generally, Canada & Pringle 1995; Rayman & Brett 1995). Competition, thus, is essential to reaffirming basic aptitudes beside men, and is significant in differential outcomes.

  1. That Remark Wasn’t Meant For Me

During the interviewing process, it became apparent that there was a senior CS faculty member who had been repeatedly identified as a source of commentary that was inappropriate within the classroom. The senior professor holds a Ph.D in psychology and a J.D., and he currently teaches computer-human interaction within CS department. The author has taken courses from the professor and had never known of such commentary. Respondents when asked about the professor’s blatant sexist remarks and innuendo made during class refused to acknowledge that they were part of the set of women to which the comments were directed:

Q: Did he make comments in class with respect to women?

A: Yes he did.

[…]

Q: The question was did you feel you were treated differently in this class?

A: Oh, yes. Well, he never treated me any different.

(Interview #713).

One respondent claimed that even though he was “sexist” that he was a “nice guy” to know personally, even through she did not know his motives for “joking around.”

Q: Did you have any experiences where professors laugh at you in private

conversations, for whatever reasons?


A: Yeah. No, I mean, just joking around. […] He was very chauvinistic, I guess.

He would make comments about women in class. […] I was sitting right

there, and I felt I was appalled when I met him. But he’s the greatest guy....

[…]

Q: Why does he need to joke around?

A: I don’t know.

(Interview #229).

Upon closer examination, respondents refused to take comments at “face value,” a protective strategy often used in “chilly climates” (See Teague 2000; Dryburgh 2000). One younger respondent cynically mused that joking would increase class attendance by a predominantly male audience, while another peer simply thought he was “chauvinist.” Lorna et al. (1998) criticizes such complacent “discourses” as they are rooted in the meritocratic and egalitarian fruits of feminist movements. Both older women, however, thought comments were flatly “sexist” and grossly offensive.

  1. “Flexibility To Be Creative”

For all respondents, CS allowed for a creativity to express itself using “rules” in a language puzzle. The concept of manipulating ideas within a frame of rules intrigued all respondents:

Q: That was fun, wasn’t it?

A: […] I think that was what got me excited. It was I could do something

beside—I could make something happen, rather than have the computer … It

gives me flexibility to be creative.

(Interview #713).

Q: So you liked to be able to make the tool do what you wanted it to do? […]

A: I think it’s really kind of fascinating. It’s like a puzzle. […] You have a

problem you want to solve.


(Interview #523).

A: […] He told me that every language has some kind of commonality. And you

just have to find it, and once you learn one language, everything is based off of it.

There are just different rules ….


(Interview #229).

The literature is devoid of the sheer thrill that these women experienced the first time that they programmed—being able to “construct something from nothing,” as one respondent remarked. Curiosity, a psychological factor not considered in this study, probably explains the fun and intricate logical “puzzles” of structured programming (See Teague 2000). Creativity in technology may be explained as an empowering agent (Clegg 1999). Henwood (1998:39-40) alludes to feminine agentic dimensions of technology knowledge, but does not develop her ideas outside of the “discursive production of gender and (hetero)sexuality.” In this regard, being creative is taking control of a means to create something “fun.”

  1. All It Takes is Diligence

The most crucial attitude needed for persistence in the CS program was diligence

and patience, as rightfully acknowledged by respondents:

A: […] The vast majority of computer science is diligence. Because it’s just

intense, if you don’t stay and keep working on your program … and keep working

on it … and keep working on it, you’ll never get it to work.


(Interview #713).

This finding is not surprising given the diligence it takes to endure many obstacles in successfully negotiating gendered achievement (Strage 1999; Lorna 1998; Ware et al. 1985). However, the socio-psychological dimensions of “attrition” and perseverance have not been fully examined in the literature (Rayman & Brett 1995). Strage (1999) deserves further evaluation when considering respondent data, as there are several attritional components regulating self-esteem and self-efficacy which were not evaluated for this study, and certainly play a major role in undergraduate success strategies.

  1. Met a Guy … Changed My Life

For both older women, marrying was accompanied with a delay in their schooling, while both younger women expressed the possibility for raising children, but only after completing their careers. Motherhood was a greater concern during their schooling.

A: I got a job, met a guy. Changed my life, of course.

Q: “Of course,” what do you mean by that?

A: Isn’t every woman’s story, you met a guy and everything fell apart?

Q: In your life, did that happen?

A: Yeah.

(Interview #263).

These types of beliefs, and unfortunate experiences, held by older women in the study sample underscore the stereotype that a woman must surrender her career in order to fully realize her potential as a mother (Dryburgh 2000). The fear that such an encounter with a sour relationship might forestall career aspirations is rooted in attainment research (Rayman & Brett 1995), and it is linked to larger patterns of occupational segregation after college (Jacobs 1996). What is crucial is a “balance” between career and family, something within the realm of possibility (Lorna et. al 2003).

Discussion and Conclusion

In devising the study, the author did not anticipate finding the blatantly crude forms of gender stereotypes that were reported in professor-student interaction by all respondents. Often such comments are hidden within the teacher-student structure more subtly (Canada & Pringle 1995). These types of male dominating behaviors form a “chilly climate” for academic progress, and, more times than not, rather than shoving persistent women out of the field, they force a female CS major to question her own gender in relation to those around her. Such a gender consciousness is both liberating and constraining, for while embracing her own unique qualities, she must simultaneously deny the full measure of her own femininity, her own sense of being a woman in order to create an identity that is able to maneuver alongside male colleagues. (See, generally, Eckes & Trautner 2000).

This study has been implemented from a larger, more specialized, and longitudinal qualitative framework within Lorna et al. (1998). But more research of the kind Kay (1992) calls “developmental theory,” focusing on understanding the social-psychological mediating agents for behavior, is greatly needed, especially in the CS field. Self-efficacy and curiosity need to be jointly pursued to explain the spark that lies at the root of “fun” in CS.

All three research questions concerning motivation, experience, and feeling arose within the mesh of research data; although the framing of the questions did not anticipate the overlap that occurred. The research questions are meant to inform further research rather than to be “answered” in any traditional sense (Merriam 1998). Nevertheless, direct correlation from data collected was made with Lorna et al. (1998) which can be summarized:

One general administrative policy suggestion is for lower classroom levels, preferably in laboratory settings where emergent academic confidence can grow, precisely what Canada & Pringle (1995) suggest for general non-technical mixed-sex classes. One study found that adapting the professor’s instructional style to fit the unique needs of either ”abstract-thinking” or “concrete-thinking” students increased the motivation and achievement in a lower-division CS courses (Hancock et al. 2002). The pragmatic attitudes about programming from study respondents fall inside the results of other research indicating that women tend to prefer more grounded explanations of abstract ideas. Another policy suggestion would advocate that classroom instruction be tailored to these orientations and that testing for self-efficacy be used (See also, Cassidy & Eachus 2002).

Had the study been broadened to account for race, ethnicity, and class, results might have differed. White women tend to view gender stereotyping differently than black women, who often view it in terms of power (Eckes & Trautner 2000). Income considerations play a fundamental role in forming the bases of privilege that are called when a woman is placed in a subordinate position and are a means of perpetuation of change (Jacobs 1996). The fact that all respondents were from middle or upper-middle class families limits the scope and applicability outside the immediate realm of this particular university setting. A larger expanded study is in order to examine how differences in socioeconomic status affect strategies within a chilly climate (e.g., how black women in CS are able to negotiate gender and race components).

References

Berg, B. 2001. Qualitative Research Methods for the Social Sciences. Boston: Allyn 
and Bacon. 
 
             
Brosnan, M. 1998. The Impact of Psychological Gender, Gender-Related Perceptions, 
Significant Others, and the Introducer of Technology Upon Computer Anxiety in
Students. Journal of Educational Computing Research. 18(1):63-78. 
 
             
Canada, K., and Pringle, R. 1995. The Role of Gender in College Classroom Interactions:
A Social Context Approach. Sociology of Education. 68(July):161-186.
 
             
Cassidy, S., and Eachus, P. 2002. Developing the Computer User Self-Efficacy (CUSE) 
Scale: Investigating the Relationship Between Computer Self-Efficacy, Gender, 
And Experience with Computers. Journal of Educational Computing Research. 
26(2):133-153.
 
             
Charlton, J. and Birkett, P. 1999. An Integrative Model of Factors Related to Computing
Course Performance. Journal of Educational Computing Research. 20(3):237-
257.
Clegg, S, and Trayhurn, D. 1999. Gender and Computing: Not the Same Old Problem.
British Educational Research Journal. 26(1):76-89.
 
             
Clegg, S. 2001. Theorizing the Machine: Gender, Education, and Computing. Gender  
and Education. 13(3):307-324.
 
             
Dryburgh, H. 2000. Underrepresentation of Girls and Women in Computer Science: 
Classification of 1990s Research. Journal of Educational Computing Research.
23(2):181-202.
 
             
Eckes, T., and Trauner, H. (Eds.) 2000. The Developmental Social Psychology of  
Gender. 	          Mahwah: Laurence Erlbaum Associates. 
 
             
Elliot, G. C. 1986. Self-Esteem and Self-Consistency: A Theoretical and Empirical 
Link Between Two Primary Motivations. Social Psychology Quarterly. 49(3):
27-218.
 
             
Etzkowski et. al. 1994. The Paradox of Critical mass for Women in Science. Science. 
266(7 October):51-54. 
 
             
Gecas, V. 1989. The Social Psychology of Self-Efficacy. Annual Review of  
Sociology. 15:291-316.
 
             
Gürer, D. 1995. Pioneering Women in Computer Science. Communications of the ACM. 
38(1):45-54.
 
             
Hancock, D., Bray, M., and Nason, S. 2002. Influencing University Students’ 
Achievement and Motivation in a Technology Course. Journal of Educational  
Research. 95(6): 365-372. 
 
             
Henwood, F. 1998. Engineering Difference: Discourses on Gender, Sexuality and Work
In a College of Technology. Gender and Education. 10(1):35-48.
 
             
House, W. 1974. Actual and Perceived Differences in Male and Female Expectancies 
and Minimal Goal Levels as a Function of Competition. Journal of Personality. 
42(3):493-510.
 
             
Jacobs, J. 1996. Gender Inequality and  Higher Education. Annual Review of
Sociology. 22:153-185.
 
             
Jennings, S., and Onwuegbuzie, A. 2001. Computer Attitudes as a Function of Age, 
Gender, Math Attitude, and Developmental Status. Journal of Educational  
Computing Research. 25(4):367-384. 
 
             
Kay, R. 1992. Understanding Gender Differences in Computer Attitudes, Aptitude, and 
Use: An Invitation  to Build Theory. Journal of Research on Computing in  
Education. 25(2):159-171.
 
             
Lorna, E., and Maurutto, P. 1998. Beyond Access: Considering Gender Deficits in 
Science Education. Gender and Education. 10(1). 
 
             
Merriam, S. 1998. Qualitative Research and Case Study Applications in Education. San 
Francisco: Josey-Bass. 
 
             
Rayman, P., and Brett, B. 1995. Women Science Majors: What Makes a Difference in 
Persistence after Graduation? Journal of Higher Education. 66(4):388-414.
 
             
Richman, E., and Shaffer, D. 2000 “If  You Let Me Play Sports”: How Might Sport 
Participation  Influence the Self-Esteem of Adolescent Females? Psychology of  
Women Quarterly. 24:189-199. 
 
             
Rossi, A. 1965. Women in Science: Why So Few? Science. 148(3674):1196-1202.
 
             
Sacks, C., Bellisimo, Y., and Mergendoller, J. 1993. Attitudes Toward Computers and 
Computer Use: The Issue of Gender. Journal of Research on Computing in  
Education. 26(2):256-269.
 
             
Shakeshaft, Charol. 1995. Reforming Science Education to Include Girls. Theory Into  
Practice. 34(1, Winter):74-79.
 
             
Silvestri, L. 1990. Expectancy of Success in Competition Against Same and Opposite 
Sex Opponents. Education. 110(3):364-369. 
 
             
Strage, A. 1999. Agency, Communion, and Achievement Motivation. Adolescence. 32(126): 299-312.
 
             
Teague, J. 2000. Women in Computing: What Brings Them to It, What Keeps Them in 
It? GATES 5(1): 45-59. 
 
             
Ware, N.C., Steckler, N.A., and Leserman, J. 1985. Undergraduate Women: Who
Chooses a Science  Major? Journal of Higher Education. 56(1): 73-84.
 
             
Young, B. 2000. Gender Differences in Student Attitudes toward Computers. Journal of  
Research on Computing in Education. 33(2): 204-216.
 
             
Zhang, V., and Espinoza, S. 1997.  Affiliations of Computer Self-Efficacy and Attitudes 
with Need for Learning Computer Skills. Journal of Educational Computing  
Research. 17(4):371-383.
 
             
 
             
 

Appendix A - Figures of Theoretical Links Between Women and Computing

APPENDIX A – Figures of Theoretical Links Between Women and Computing

 

Appendix B - Interview Guide

  1. Background

    1. How old are you?

    2. Why did you decide to come to school here? How does it compare (positively and negatively) to previous schools you have attended (i.e., secondary)?

    3. What are the best and worst aspects of living here? At this university?

    4. How long have you been a computer science major?


  1. Motivations

  1. Why did you decide to pursue a place in the computer science field?

  2. Do you have any special role model or mentor whose advice and/or direction is very

important to you? How so and to what extent?

  1. What will completing your degree program give you? Money, status, power, happiness?

  2. Earlier in your career (before arriving here) did you feel pressure to not complete your degree plan? If so, from where, how much and to what extent?

  3. What does your family think of your chosen field? Do you agree?

  4. What are the advantages and disadvantages of being a woman in the computer science field?

  1. Experiences

Classroom Environments & Interaction

  1. Did you enjoy introductory computer science courses? Why or why not?

  2. If the professor was male or female, did you feel interaction during and after class, easier or more difficult? Explain.

  3. Have most of your classmates been primarily male or female? Why is this?

Discrimination & Prejudice

  1. Have you ever felt discriminated against because of your gender? What happened? How did you react to it?

  2. Describe any specific expectations by professors and peers of women. Did you feel they treated you “differently”? If so, why would they treat a woman differently?

  1. Feelings & Self Image

  1. Do you feel that some people view computer science as a “masculine” field? If so, how do you “negotiate” your femininity?

  2. Do you feel competitive when in collaborative group projects? If so, is this feeling greater towards women than to men?

  3. Do you feel you can maintain a level of competency that is “expected” of you during projects and/or individual work? More so or less with mostly women in your group?

  4. Have you formed any personal relationship with any of your classmates? Dating? Study partners? How?

  5. Have you made “sacrifices” to “fit in” with your classmates? If so, what?

  1. Concluding question(s)

  1. How would you improve classroom and/or educational conditions and/or opportunities in university-level computer science education, if given the opportunity? What changes would you make?

  2. What advice do you have for future female computer science majors?


Appendix C - Concent Form

You are invited to participate in a study of _______ University female Computer Science majors. I am a student working on a final research project for a Sociology class (SOCI 3309). You will be one of 4 people chosen to participate in this study. During the course of this study, I may ask you questions about your experiences, motivations, and feelings related to, for example, your treatment in introductory computer science courses, motivations to complete your program, mentors/role models, and study habits.

Should you decide to participate in this study, you will take part in an in-depth interview with me. The interview will be conducted in a nearby restaurant, coffeeshop, or another suitable location that we choose and will be tape-recorded. The interview should take no more than one hour of your time. The possible risk of your participation is psychological harm from describing/re-living past events, situations, and interactions that may have been negative and/or damaging. At the end of the interview, I can give you a list of agencies providing services you may potentially need.

Any information that is obtained in connection with this study, and that may be identified with you, will remain strictly confidential. Tapes will be assigned a code number so your name will never be attached to any tape. Tapes will be heard only by me, the interviewer. When I describe the information obtained in my study, an alias or false name will be used in place of your true name or identity.

If you decide to take part in the interview, you are free to stop the interview at any time. You don’t have to answer any question that makes you uncomfortable. If you have any questions, please ask me. If you have any additional questions, feel free to contact Dr. Patti Giuffre. You are provided a copy of this form to keep.

You are making a decision on whether or not to participate in this study. Your signature indicates that you have read the information provided above and have decided to participate. Should you choose to do so, you may withdraw your participation at any time after signing this form.

__________________________________

Signature of Participant - Date

__________________________________

Signature of Investigator - Date


Appendix D - Second Email Correspondence

Dear <Student>:

Thanks again for your positive response to my inquiry.  As previously
mentioned, the Department of Sociology is conducting a study on
the subjective experiences, motivations, and feelings of college women in
computer science.  By participating you can potentially help our understanding of how to better address issues directly relating to women that are sometimes forgotten
within computer science.  Anonymous 1-hr interviews will be conducted
with the study coordinator, Al Youngblood (<Phone>), during the week
following Spring Break.

The following interview blocks during the week of March 17-21st are
available for a 1 hr interview:

     Monday, Wednesday Morning         10AM- 12PM
     Monday, Wednesday Evening          3PM-6PM
     Tuesday Afternoon                          1PM - 6PM
     Friday Afternoon                              ALL DAY

Before Mr. Youngblood can begin, however, he wants to confirm your
definite interest in participating.  Within the week I encourage you to confirm a
tentative time and date directly with him via email at <email>.   He will contact you with further information after receiving your confirmation.

<CS Faculty>

 

Appendix E - Transcripts

[105 Pages Removed to Protect Confidential Data]

 

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My email address is if you want to contact me. I am usually available via email on a regular basis.