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Computing Educational Activities Involving People Rather Than Things Appeal More to Women (Recruitment Perspective)
... Instead, stereotypes and social expectations play a role in forming values and beliefs, which in turn influence students' educational and career choices and success [18]. This understanding is crucial when considering the Things-People dimension, which has been used to explain differences in men's and women's preferences [22,83]. While it has been observed that "men may show a preference for working with things and women for working with people" with women expressing stronger 'Social' and 'Artistic' interests than men, these trends should not be viewed as essential differences. ...
... The finding that HCI (Human-Computer Interaction) is the most popular non-foundational elective with women is consistent with research on the Things-Humans dimension, suggesting that women are more interested in humans than are men and that men are more interested in things [22,83]. However, at fourth place in men's top five, men were not far behind women in terms of the appeal of HCI classes. ...
The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students and which elective topics are more popular with women students.
... This is further supported by research suggesting that tailoring introductory programming teaching is important to support interest in computing (Chakrabarty & Martin, 2018;Ott et al., 2018). Specifically, the education should be augmented by engagement in scenarios which meet students' interests, talents, and career goals (Thiry et al., 2011;Christensen et al., 2021). ...
Decades of technological development and innovation have led to an unprecedented digitalization of society. Graduates entering the modern workforce now need better computational competences. Higher education is thus forced to adapt and consider how to support these demands. To support educators in making decisions regarding how to integrate computing in their programmes, we set out to investigate to what extent and how mandatory (computer) programming has emerged in the tertiary educational system of an entire country: Denmark. We analyzed all course descriptions from 1169 tertiary educational programmes at all higher educational institutions spanning all of Denmark. This provides a “snapshot” of where and how programming education has emerged and manifested itself in higher education across faculties, study programmes, and disciplines for an entire country. Our results demonstrate that, as of 2023, 1 in 6\nicefrac {1}{2} educational programmes (175 out of 1169) include mandatory programming. To support educators in introducing programming, we quantify and provide an overview of educational programmes with mandatory programming along several dimensions. We characterize the roles programming has in different programmes, how programming is delineated, and which families of competences are often taught in connection with programming. Based on the results of our study, we issue five recommendations directed at policymakers and educators responsible for navigating the inclusion of programming in their education.
... The group fairness requires different groups to be treated similarly [11][12][13]. For example, to avoid discrimination in recruitment recommendations, Marcher et al. [28] provide equal employment opportunity for different gender groups; Rahmani et al. [11] propose a usercentered fairness re-ranking framework to mitigate its unfair behavior towards a certain user group i.e., disadvantaged group. Therefore, research on group fairness can effectively improve the fairness of disadvantaged group. ...
The remarkable progress of machine learning has had a significant impact on decision-making, thus fairness is an important topic. Existing fair recommendation methods generally design a constraint framework between consumer satisfaction or item exposure in the optimization model. However, these methods only focus on the fairness of consumers and items, but fail to ensure the fairness of platforms. Ignoring platform fairness may lead to an imbalance between consumer satisfaction and platform profit. In this paper, we propose a novel recommendation method based on fairness called CPG-FairRec, which consists of three modules: Data division module, Global fairness-aware module and Local fairness-aware module. Data division module divides the consumers and items into two groups based on spendings and prices, respectively. Global fairness-aware module learns the difference_score of the item group and consumer group based on the consumer’s historical behavior, and continuously balance the satisfaction of consumer group and the exposure of item group. Local fairness-aware module aims to optimize the exposure of individual item through the greedy algorithm based on the item price to achieve higher platform profit. We conduct experiments on two real-world datasets and results demonstrate the superiority of the CPG-FairRec in recommendation quality and profitability.
... All these embedded stereotypes are further nurtured by the way ICT is portrayed in the media such as TV, films, games and advertising (Lamers and Mason, 2018). Similarly, Christensen et al. (2021) in their experimental study investigated the gender differences in the "People-Things" dimension among high school students in relation to ICT education. Their results show that women found "People-themed activities" more appealing compared to men, who preferred "Things-oriented activities" more than women. ...
Rapid development of digital technologies has stemmed profound changes in the society, positioning the ICT sector as a key driver and contributor. This sector, including education, is however characterized by a gender gap, which is problematic in the light of the increasing demand for digital competence and the ability to move toward a sustainable egalitarian society. In this study, we argue for a need to explore the concept of ICT in higher education. This involves assessing the success of educational programs in attracting women and exploring the perceptions of female students regarding their academic environment. With a specific focus on Sweden, through a survey involving 82 respondents, we provide evidence on motivations and perceptions of women regarding leading choices on pursuing ICT higher education. We propose a holistic approach to studying gender representation and inclusion in ICT higher education, with a focus on women’s perceptions, experiences, and suggestions.
... Looking into the commonalities in these programs, the availability of mentors who were near-peer role models along with increased attention to equity, diversity, and inclusion made a difference (Cummings et al., 2021;Ericson & McKlin, 2012;Ericson et al., 2016). Additionally, introduction to computing through culturally and socially relevant programs, implementing inclusive pedagogy, and incorporating socially meaningful activities into the courses might have led girls to higher achievement in AP CS programs (Boda & McGee, 2021;Christensen et al., 2021;Ericson & McKlin, 2018;Goode & Chapman, 2011;Marcher et al., 2021;Margolis et al., 2008). ...
Using the test scores of more than 1,000,000 students who participated in the Advanced Placement Computer Science (AP CS) exams from 1997 to 2020, this study examined the direction and magnitude of the trends in gender disparity in participation and top achievement in advanced exams. The findings indicated that the male-to-female ratio (MFR) among AP Computer Science (CS) exam participants declined from 4.87 to 2.26 between 1997 and 2020. Similarly, the MFR among top scorers (students who scored 5 out of 5) in any type of AP CS exams declined rapidly, in favor of female students, from 8.00 to 2.14 during the same period. Possible implications of these findings for educators, particularly for AP CS teachers and school counselors, were also discussed in the context of the underrepresentation of females in computing fields.
Secondary analyses of Revised NEO Personality Inventory data from 26 cultures (N = 23,031) suggest that gender differences are small relative to individual variation within genders; differences are replicated across cultures for both college-age and adult samples, and differences are broadly consistent with gender stereotypes: Women reported themselves to be higher in Neuroticism, Agreeableness, Warmth, and Openness to Feelings, whereas men were higher in Assertiveness and Openness to Ideas. Contrary to predictions from evolutionary theory, the magnitude of gender differences varied across cultures. Contrary to predictions from the social role model, gender differences were most pronounced in European and American cultures in which traditional sex roles are minimized. Possible explanations for this surprising finding are discussed, including the attribution of masculine and feminine behaviors to roles rather than traits in traditional cultures.
Retaining students in computer science (CS) courses and majors is a concern for many undergraduate CS programs in the United States. A large proportion of students who initially declare a major in CS do not complete a CS degree. The impact of future-oriented motivational constructs such as career aspirations and future connectedness on retention has received relatively little research attention, but these are potential contributors to students' retention in CS courses. The purpose of this study was to investigate how future-oriented motivation related to CS students' retention in CS courses over three consecutive semesters. Students enrolled in CS courses (four 100-level courses, one 200-level course, three 300-level courses, and five 400-level courses) completed survey measures of future-oriented motivation, and course enrollment data were collected for the three semesters. Logistic regression was used to determine whether motivation variables could distinguish between students who were enrolled in at least one CS course during a given semester and students who were not enrolled in any CS courses. Results indicate that, across all three semesters, career aspirations and knowledge of CS career paths were associated with a greater likelihood of continuing to take CS courses, and stronger future connectedness was associated with a lower likelihood of continuing to take CS courses. Implications for CS educators are discussed.
Researchers have been working to understand the high dropout rates in computer science (CS) education. Despite the great demand for CS professionals, little is known about what influences individuals to complete their CS studies. We identify gains of studying CS, the (learning) environment, degree’s usefulness, and barriers as important predictors of students’ intention to complete their studies in CS (retention). The framework aims to identify reasons that may contribute to dropout, using responses from 344 CS students. The eight-predictor model accounts for 39 % of the explained variance in student retention. A high level for degree’s usefulness has a positive effect on retention. Further, cognitive gains and supportive environment positively impact degree’s usefulness, while non-cognitive gains hinder it. Lastly, negative feelings (personal values) are found to reduce student retention. The overall outcomes are expected to contribute to theoretical development, in order to allow educators and policy makers to take appropriate measures to enhance students’ experience in CS studies and increase retention.
Women obtain more than half of U.S. undergraduate degrees in biology, chemistry, and mathematics, yet they earn less than 20% of computer science, engineering, and physics undergraduate degrees (National Science Foundation, 2014a). Gender differences in interest in computer science, engineering, and physics appear even before college. Why are women represented in some science, technology, engineering, and mathematics (STEM) fields more than others? We conduct a critical review of the most commonly cited factors explaining gender disparities in STEM participation and investigate whether these factors explain differential gender participation across STEM fields. Math performance and discrimination influence who enters STEM, but there is little evidence to date that these factors explain why women’s underrepresentation is relatively worse in some STEM fields. We introduce a model with three overarching factors to explain the larger gender gaps in participation in computer science, engineering, and physics than in biology, chemistry, and mathematics: (a) masculine cultures that signal a lower sense of belonging to women than men, (b) a lack of sufficient early experience with computer science, engineering, and physics, and (c) gender gaps in self-efficacy. Efforts to increase women’s participation in computer science, engineering, and physics may benefit from changing masculine cultures and providing students with early experiences that signal equally to both girls and boys that they belong and can succeed in these fields.
Greater male variability has been established in cognitive abilities and physical attributes. This study investigated sex differences in variability in vocational interests with two large samples (N > 40 000 and N > 70 000). The results show that although men varied more in Realistic and Enterprising interests, women varied more in Artistic and Conventional interests. These differences in variability had considerable influence on the female–male tail ratios in vocational interests that have been found to contribute to reported gender disparities in certain fields of work and academic disciplines. Moreover, differences in means and variability interacted non-linearly in shaping tail-ratio imbalances. An age-specific analysis additionally revealed that differences in variability diminished with age: Older samples showed smaller differences in variance in Realistic, Artistic, and Social interests than younger samples. Thus, I found no evidence that greater male variability applies for vocational interests in general. Copyright © 2015 European Association of Personality Psychology
This article explores the factors influencing high school students' career aspirations with a study analyzing 141 high school students. The Social Cognitive Career Development Model was utilized to examine the interactive relationships among learning experiences, career self-efficacy, outcome expectations, career interests, and career choices. The results of a structural equation modeling analysis supported the mediating role of career self-efficacy in the career decision-making process, but the specific paths among the predicting variables to career aspirations were found to be different for female and male high school students. Implications for school counselors to provide more effective career intervention programs are discussed.
This study addresses why women are underrepresented in Computer Science (CS). Data from 1319 American first-year college students (872 female and 447 male) indicate that gender differences in computer self-efficacy, stereotypes, interests, values, interpersonal orientation, and personality exist. If students had had a positive experience in their first CS course, they had a stronger intention to take another CS course. A subset of 128 students (68 females and 60 males) took a CS course up to one year later. Students who were interested in CS, had high computer self-efficacy, were low in family orientation, low in conscientiousness, and low in openness to experiences were more likely to take CS courses. Furthermore, individuals who were highly conscientious and low in relational-interdependent self-construal earned the highest CS grades. Efforts to improve women’s representation in CS should bear these results in mind.
Despite the best laid plans, counterproductive student behavior can interfere with faculty establishment of supportive classroom climates. This paper describes methods for framing the climate of the computer science classroom to minimize outspoken students' unwanted displays of intellectual prowess and engender co-learning behavior among students. Explicit framing of a supportive climate reduces student anxiety about their status among peers, leads them to expect to co-learn concepts, and reduces trepidations about speaking up in class. The framing is grounded by preemptively establishing expectations and addressing concerns through student discussion; asking students to go outside of their interaction style comfort zones for speaking in class; and explicitly describing teaching choices and classroom processes. The framing is reinforced by exposing wrong answers as useful rather than embarrassing, turn-taking techniques for equal student participation, and collaborative learning for assignments and in-class problem solving. Classroom-based retention techniques are important for retaining students who are less experienced with computer science and unsure how to interpret peers? public knowledge claims in relation to their own knowledge or faculty expectations.
A study analyzing survey results of more than 850 students enrolled in college computer courses was conducted. The survey included, among other things, questions about students' preferences for a programming assignment. The three choices given were examples of common types of programming assignments found in CS1 textbooks: an application to "real-world" activities, a game program, and a mathematical problem to be solved. This study reports the preferences of all students (includes 65 different majors) answering the survey question and also compares the results with the CS majors. Gender differences were evident from the study, and implications for developing assignments to better motivate and involve various groups of students in Computer Science programming classes are discussed.
—Amid ongoing public speculation about the reasons for sex differences in careers in science and mathematics, we present a consensus statement that is based on the best available scientific evidence. Sex differ-ences in science and math achievement and ability are smaller for the mid-range of the abilities distribution than they are for those with the highest levels of achievement and ability. Males are more variable on most measures of quantitative and visuospatial ability, which necessarily results in more males at both high-and low-ability extremes; the reasons why males are often more variable remain elusive. Successful careers in math and science require many types of cognitive abilities. Females tend to excel in verbal abilities, with large differences between females and males found when assessments include writing samples. High-level achievement in science and math requires the ability to communicate effectively and comprehend abstract ideas, so the female advantage in writing should be helpful in all academic domains. Males outperform females on most measures of visuospatial abilities, which have been implicated as contributing to sex differences on standardized exams in mathematics and science. An evolutionary account of sex differences in mathematics and science supports the conclusion that, although sex differences in math and science performance have not directly evolved, they could be indirectly related to differences in interests and specific brain and cognitive systems. We review the brain basis for sex differences in science and mathematics, describe consistent effects, and identify numerous possible correlates. Experience alters brain structures and functioning, so causal statements about brain differences and success in math and science are circular. A wide range of sociocultural forces contribute to sex differences in mathematics and science achievement and ability—including the effects of family, neighborhood, peer, and school influences; training and experience; and cultural practices. We conclude that early experience, biological factors, educational policy, and cultural context affect the number of women and men who pursue advanced study in science and math and that these effects add and interact in complex ways. There are no single or simple answers to the complex questions about sex differences in science and mathematics.
Despite the ubiquity of harmful math attitudes that disadvantage girls, girls are now performing just as well as boys in math
in the U.S. (Hyde et al. 1990; Hyde et al. 2008). At the same time, stark gender disparities remain in who chooses to pursue math-related careers (National Science Foundation
2009). Why have gender disparities persisted in some math-related domains but not others? I suggest that considering the extent
to which math-related domains are stereotyped as masculine can help explain why women do not seek out math-related careers,
even as they perform just as well in math. Changing current stereotypes of math-related careers to make them less incongruent
with the female gender role may help to recruit more women into these careers.
KeywordsGender disparities–Stereotypes–Underrepresentation–Gender roles–Education
This study examined the nature of sex stereotypes of occupations as they exist among college students today. The method of eliciting sexual stereotypes of occupations was distinctive in that three types of rating criteria were used, each emphasizing a different aspect of perception, on the basis of which the ratings of occupations as masculine, feminine, or neutral were subsequently made. The results indicate that sexual stereotypes of occupations are clearly defined and held in agreement by both college men and college women. The study yielded information about the mean rating of each of 129 occupations in terms of its masculinity, femininity, and neutrality.
Item response theory was used to address gender bias in interest measurement. Differential item functioning (DIF) technique, SIBTEST and DIMTEST for dimensionality, were applied to the items of the six General Occupational Theme (GOT) and 25 Basic Interest (BI) scales in the Strong Interest Inventory. A sample of 1860 women and 1105 men was used. The scales were not unidimensional and contain both primary and minor dimensions. Gender-related DIF was detected in two-thirds of the items. Item type (i.e., occupations, activities, school subjects, types of people) did not differ in DIF. A sex-type dimension was found to influence the responses of men and women differently. When the biased items were removed from the GOT scales, gender differences favoring men were reduced in the R and I scales but gender differences favoring women remained in the A and S scales. Implications for the development, validation and use of interest measures are discussed.
In addition to "learning by doing," programming assignments and projects are also the mechanism by which students learn about the utility of computer science - or not. Recent research indicates that the current generation of students is in search of a career with meaning, and women and minorities have long been known to desire careers that help society. In this paper, we provide student testimonials on the importance and benefits of practical and socially-relevant assignments. We then examined approximately 200 first year (CS1) and software engineering assignments at top computer science institutions. Only 34% of the CS1 projects had a practical or socially-relevant context, 41% had no context at all, and 15% were games. For software engineering projects, 62% were practical or socially-relevant, but still 16% had no practical context. We recommend that educators, through their assignments, place increased emphasis on demonstrating that computer science can be used to aid society and/or produce products of practical value to society.
Computer science is seeing a decline in enrollment at all levels of education. One key strategy for reversing this de- cline is to improve methods of student retention. This pa- per, based on a 10-month case study at the Department of Computer Science at the University of Illinois at Urbana- Champaign, examines two aspects of student retention at both the graduate and undergraduate levels: community identity and community relationships. Our data shows that students feel isolated from each other, faculty, and mem- bers of the greater computer science community. Given our findings, we highlight existing programs and propose new programs which improve student-community interactions. While the lessons learned might not apply at every insti- tution, they constitute a valuable case study for improving conditions for students at large research universities.
An important problem faced by many Computer Science and Software Engineering programs is declining enrollment. In an effort to reverse that trend at Mississippi State University, we have instituted pair programming for the laboratory exercises in the introductory programming course. This paper describes a study performed to analyze whether using pair programming would increase retention. An important goal of this study was not only to measure increased retention, but to provide insight into why retention increased or decreased. The results of the study showed that retention significantly increased for those students already majoring in Computer Science, Software Engineering, or Computer Engineering. In addition, survey results indicated that the students viewed many aspects of pair programming to be very beneficial to their learning experience.
This paper investigates gender differences in personality traits, both at the level of the Big Five and at the sublevel of two aspects within each Big Five domain. Replicating previous findings, women reported higher Big Five Extraversion, Agreeableness, and Neuroticism scores than men. However, more extensive gender differences were found at the level of the aspects, with significant gender differences appearing in both aspects of every Big Five trait. For Extraversion, Openness, and Conscientiousness, the gender differences were found to diverge at the aspect level, rendering them either small or undetectable at the Big Five level. These findings clarify the nature of gender differences in personality and highlight the utility of measuring personality at the aspect level.
In studies on gender differences with respect to Holland's (1997) vocational interests, men often score higher on Realistic (and sometimes also on Investigative and Enterprising) interests, and women score higher on Social and Artistic interests. In research and practice, vocational interests are almost exclusively assessed by means of questionnaires. The aim of this study was to examine whether these results are also stable across assessment methods. Thus, a questionnaire and a nonverbal interest inventory were administered to a sample of N = 448 participants. Results were in the expected direction for both inventories, men scoring higher on Realistic interests and women scoring higher on Artistic and Social interests. However, the structure of interests varied between men and women. The structural assumptions of Holland's theory fit the data for men better than the data for women. Thus, mean-level gender differences should be interpreted conservatively.
The magnitude and variability of sex differences in vocational interests were examined in the present meta-analysis for Holland's (1959, 1997) categories (Realistic, Investigative, Artistic, Social, Enterprising, and Conventional), Prediger's (1982) Things-People and Data-Ideas dimensions, and the STEM (science, technology, engineering, and mathematics) interest areas. Technical manuals for 47 interest inventories were used, yielding 503,188 respondents. Results showed that men prefer working with things and women prefer working with people, producing a large effect size (d = 0.93) on the Things-People dimension. Men showed stronger Realistic (d = 0.84) and Investigative (d = 0.26) interests, and women showed stronger Artistic (d = -0.35), Social (d = -0.68), and Conventional (d = -0.33) interests. Sex differences favoring men were also found for more specific measures of engineering (d = 1.11), science (d = 0.36), and mathematics (d = 0.34) interests. Average effect sizes varied across interest inventories, ranging from 0.08 to 0.79. The quality of interest inventories, based on professional reputation, was not differentially related to the magnitude of sex differences. Moderators of the effect sizes included interest inventory item development strategy, scoring method, theoretical framework, and sample variables of age and cohort. Application of some item development strategies can substantially reduce sex differences. The present study suggests that interests may play a critical role in gendered occupational choices and gender disparity in the STEM fields.
In 3 studies (respective Ns = 289, 394, and 1,678), males and females were assessed on Big Five traits, masculine instrumentality (M), feminine expressiveness (F), gender diagnosticity (GD), and RIASEC (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) vocational interest scales. Factor analyses of RIASEC scores consistently showed evidence for D.J. Prediger's (1982) People-Things and Ideas-Data dimensions, and participants' factor scores on these dimensions were computed. In all studies Big Five Openness was related to Ideas-Data but not to People-Things. Gender was strongly related to People-Things but not to Ideas-Data. Within each sex, GD correlated strongly with People-Things but not with Ideas-Data. M, F, and Big Five measures other than Openness tended not to correlate strongly with RIASEC scales or dimensions. The results suggest that gender and gender-related individual differences within the sexes are strongly linked to the People-Things dimension of vocational interests.
Secondary analyses of Revised NEO Personality Inventory data from 26 cultures (N = 23,031) suggest that gender differences are small relative to individual variation within genders; differences are replicated across cultures for both college-age and adult samples, and differences are broadly consistent with gender stereotypes: Women reported themselves to be higher in Neuroticism, Agreeableness, Warmth, and Openness to Feelings, whereas men were higher in Assertiveness and Openness to Ideas. Contrary to predictions from evolutionary theory, the magnitude of gender differences varied across cultures. Contrary to predictions from the social role model, gender differences were most pronounced in European and American cultures in which traditional sex roles are minimized. Possible explanations for this surprising finding are discussed, including the attribution of masculine and feminine behaviors to roles rather than traits in traditional cultures.
This research examines self-stereotyping in the context of multiple social identities and shows that self-stereotyping is a function of stereotyped expectancies held in particular relationships. Participants reported how others evaluated their math and verbal ability and how they viewed their own ability when their gender or ethnicity was salient. Asian American women (Experiment 1) and European Americans (Experiment 2) exhibited knowledge of stereotyped social expectancies and corresponding self-stereotyping associated with their more salient identity. African Americans (Experiment 3) exhibited some knowledge of stereotyped social expectancies but no corresponding self-stereotyping. Correlational evidence and a 4th experiment suggest that self-stereotyping is mediated by the degree to which close others are perceived to endorse stereotypes as applicable to the self.
The untold history of women and computing: how pioneering women succeeded in a field shaped by gender biases.
Today, women earn a relatively low percentage of computer science degrees and hold proportionately few technical computing jobs. Meanwhile, the stereotype of the male “computer geek” seems to be everywhere in popular culture. Few people know that women were a significant presence in the early decades of computing in both the United States and Britain. Indeed, programming in postwar years was considered woman's work (perhaps in contrast to the more manly task of building the computers themselves). In Recoding Gender, Janet Abbate explores the untold history of women in computer science and programming from the Second World War to the late twentieth century. Demonstrating how gender has shaped the culture of computing, she offers a valuable historical perspective on today's concerns over women's underrepresentation in the field.
Abbate describes the experiences of women who worked with the earliest electronic digital computers: Colossus, the wartime codebreaking computer at Bletchley Park outside London, and the American ENIAC, developed to calculate ballistics. She examines postwar methods for recruiting programmers, and the 1960s redefinition of programming as the more masculine “software engineering.” She describes the social and business innovations of two early software entrepreneurs, Elsie Shutt and Stephanie Shirley; and she examines the career paths of women in academic computer science.
Abbate's account of the bold and creative strategies of women who loved computing work, excelled at it, and forged successful careers will provide inspiration for those working to change gendered computing culture.
There is high demand for qualified Information and Communication Technology (ICT) practitioners in the European labor market. In Estonia, the problem is not a low number of ICT students but a high dropout rate. The aim of this study is to find how it is possible to predict first-year dropout in higher education ICT studies and possibly to engage methods to decrease dropout rate. Data was collected from 301 first-year ICT students in Estonia who filled in a questionnaire at the beginning of the first semester and after the first semester. Additionally, some information was collected electronically during the admission process. The results showed that on average, 32.2% of the ICT students in Estonia dropped out during the first study-year. It was found that students who dropped out had lower scores in the state mathematics exam. This means that the score of the mathematics exam is one characteristic that can predict dropout during the first study-year. At the beginning of the studies there were not many differences in students' perception of their interest and how well the studies met their expectations. However, the answers received after the first semester showed some statistically significant differences between the students who dropped out during the first study-year and those who did not. Differences occurred, e.g., in the case of the following questions: how big their interest in ICT was, how well the studies met their expectations, how pleasant studying was for them, and how high they felt was the probability of them finishing their studies. It can be concluded that asking questions after the first semester gives information to universities as to who are about to drop out. Based on the information universities can support their students to retain them. The results support some factors that were found in literature to be important for avoiding dropout (e.g., motivation, earned credit points, prior studies, expectations), but in some cases the results of this study are different than the literature suggests (e.g., age, gender, working during studies, number of friends in the ICT field). It could be that these factors are not that important in influencing first-year dropout in ICT studies.
Students who develop a strong interest in another field can hardly be fair target for retention efforts. There are others, however, who might do very well in this field but who leave computer science because of misunderstandings about and a negative experience in this academic major. Retention literature suggests and provides numerous examples of initiatives that improve student retention. To determine how best to impact student retention in your computer science department, a comprehensive understanding of the student experience of your major is essential. Not only is it important to know why students leave your major, but an understanding of why they stay can be useful in developing high impact initiatives.
The current study involves a comparative examination of students who graduated from the computer science (CS) major at Georgia Institute of Technology in Atlanta, Georgia (Stayers) with students who chose to leave the major (Leavers). Specifically, perceptions and experiences of computer science as a field in general and the climate of the major were studied. Comparative data collected provided insights into reasons why they entered into the major, about belief of adequate preparation and confidence going into the major, definitions of CS and other related factors. Results were analyzed to identify potential target areas and concrete implementation strategies are suggested for improved retention in the computer science major at this institution.
In the decade since Unlocking the Clubhouse: Women in Computing (MIT Press, 2002) was published, educational institutions have coalesced around the mission of increasing women's participation in computing. Yet, despite the uptick of interest in computer science majors and the surge of technology shaping all aspects of our lives, the numbers of women majoring in computer science are still abysmally small. In this talk, I will further reflect on why this is the case, and make connections to the issues raised in Stuck in the Shallow End: Education, Race, and Computing---the underrepresentation in computer science of students of color. I will examine how underrepresentation in computing relates to the larger educational crisis in this country and issues we face as world citizens. This talk is part of an overarching mission to understand how inequality is produced in this country and the types of social action required to equalize opportunities and broaden participation in computing.
The present research examines undergraduates’ stereotypes of the people in computer science, and whether changing these stereotypes using the media can influence women’s interest in computer science. In Study 1, college students at two U.S. West Coast universities (N = 293) provided descriptions of computer science majors. Coding these descriptions revealed that computer scientists were perceived as having traits that are incompatible with the female gender role, such as lacking interpersonal skills and being singularly focused on computers. In Study 2, college students at two U.S. West Coast universities (N = 54) read fabricated newspaper articles about computer scientists that either described them as fitting the current stereotypes or no longer fitting these stereotypes. Women who read that computer scientists no longer fit the stereotypes expressed more interest in computer science than those who read that computer scientists fit the stereotypes. In contrast, men’s interest in computer science did not differ across articles. Taken together, these studies suggest that stereotypes of academic fields influence who chooses to participate in these fields, and that recruiting efforts to draw more women into computer science would benefit from media efforts that alter how computer scientists are depicted.
Multidimensional scaling (MDS) analysis was used to analyze the structure of interests of the Women- and Men-in-General samples used in the revision of the 1985 Strong Interest Inventory (Hansen & Campbell, 1985). In the present study, the intercorrelation matrices of the Strong Interest Inventory General Occupational Themes, for the 1985 Women-in-General and Men-in-General Reference Samples, were separately submitted to MDS. Results from earlier analyses that found sex differences in the structure of interests (Feldman & Meir, 1976; Rounds, Davison, & Dawis, 1979; Utz & Korben, 1976) may have been confounded because matched-interest samples were not used. Subjects for the Women-in-General and Men-in-General samples used in this study were selected with the specific intention of matching the interests of females and males by choosing participants who were matched on occupational title. The obtained two-dimensional solutions demonstrated a gender difference in the underlying structure of interests for these reference groups. Possible explanations and implications of these results are discussed.
This article develops a supply-side mechanism about how cultural beliefs about gender differentially influence the early career-relevant decisions of men and women. Cultural beliefs about gender are argued to bias individuals' perceptions of their competence at var- ious career-relevant tasks, controlling for actual ability. To the extent that individuals then act on gender-differentiated perceptions when making career decisions, cultural beliefs about gender channel men and women in substantially different career directions. The hy- potheses are evaluated by considering how gendered beliefs about mathematics impact individuals' assessments of their own mathe- matical competence, which, in turn, leads to gender differences in decisions to persist on a path toward a career in science, math, or engineering.
The existence of substantial sex differences in vocational preferences is important, given the prominent role assigned vocational preferences as a link between underlying interests and vocational choice. Strong Interest Inventory (SII) responses of 16,484 males and females, ages 18 to 22, were analyzed to determine whether relationships between measured interests and vocational preferences were equivalent for the two sexes. Using differential item functioning (DIF) analysis techniques, sex-related differentials in responses to 28 SII occupational title items were estimated, after controlling for 1, 3, or 6 General Occupational Theme scale scores. Significant sex-related DIF was found on most of the occupations. Further, the sex-related DIF was strongly correlated with sextype ratings for the occupations. These results suggest that sex differences in vocational preference are not fully explained by differences in measured vocational interests, and that vocational preferences may not be equivalent indicators of underlying interests for males and females.
In this article, we analyze gender differences in college major selection for respondents to the Education Longitudinal Study (2002-2006), focusing on educational pathways through college that lead to science, engineering, or doctoral-track medicine occupations and to non-doctoral track clinical and health sciences occupations. We show that gender differences in college major selection remain substantial, even for a cohort in which rates of enrollment in postsecondary education are more than ten percent higher for young women than for young men. Consistent with other recent research, we demonstrate that neither gender differences in work-family goals nor in academic preparation explain a substantial portion of these differences. However, the occupational plans of high school seniors are strong predictors of initial college major selection, a finding that is revealed only when occupational plans are measured with sufficient detail, here by using the verbatim responses of students. We also find that the association between occupational plans and college major selection is not attributable to work-family orientation or academic preparation. Finally, we find gender differences in the associations between occupational plans and college major selection that are consistent with prior research on STEM attrition, as well as with the claim that attrition also affects the selection of majors that are gateways into doctoral-track medicine. We discuss the implications of the predictive power of occupational plans formed in adolescence for understanding sex segregation and for policies intended to create a gender-balanced STEM and doctoral-level medical workforce.
Females are less likely to take advantage of computer learning opportunities than males. Gender biases and societal stereotypes, as well as differential interests, experience and attitudes contribute to a low level of participation by females in computer courses. In order to better understand the gender imbalance and high level of female attrition in computer science, survey instruments were developed to measure attitudes toward the experience of students currently enrolled in the Computer Science Department at a large western university, and to note differences in the reasons that males and females choose to change from computer science to other majors. The study suggests that a key factor influencing the high rate of female attrition is lack of previous experience with computers before entering the program. Other factors may be gender-biased attitudes and behavior, interactions with other computer science students, and the nature of computer science as a discipline.
How big are gender differences in personality and interests, and how stable are these differences across cultures and over time? To answer these questions, I summarize data from two meta-analyses and three cross-cultural studies on gender differences in personality and interests. Results show that gender differences in Big Five personality traits are ‘small’ to ‘moderate,’ with the largest differences occurring for agreeableness and neuroticism (respective ds = 0.40 and 0.34; women higher than men). In contrast, gender differences on the people–things dimension of interests are ‘very large’ (d = 1.18), with women more people-oriented and less thing-oriented than men. Gender differences in personality tend to be larger in gender-egalitarian societies than in gender-inegalitarian societies, a finding that contradicts social role theory but is consistent with evolutionary, attributional, and social comparison theories. In contrast, gender differences in interests appear to be consistent across cultures and over time, a finding that suggests possible biologic influences.
Despite widespread changes in occupational opportunities, men and women continue to show divergent preferences for careers. This research invoked a motivational framework to explain sex-differentiated career interest. From a role congruity perspective (Diekman & Eagly, 2008), the internalization of gender roles leads people to endorse gender-stereotypic goals, which then lead to interest in occupations that afford the pursuit of those goals. Three studies provided evidence for the hypotheses. Study 1 found that male- and female-stereotypic careers were perceived to afford different goals. Studies 2 and 3 found that men and women endorsed different goals and that this gender-normative goal endorsement predicted gender-stereotypic career interest. In addition, structural equation modeling (Study 3) indicated that internalization of gender roles fully accounted for sex-differentiated goal endorsement. These findings thus extend the social role theory framework to consider processes related to self-selection into specific social roles.
Average sex differences in workplace outcomes are often assumed to be products of a malfunctioning labor market that discourages women from nontraditional occupations and a biased educational system that leaves women inadequately prepared for scientific and technical work. Rather than being a product purely of discriminatory demand, however, many sex differences in occupational distribution are at least partially a result of an imbalance in supply. Sex differences in both temperament and cognitive ability, which are products of our evolutionary history, predispose men and women toward different occupational behavior. The tendency of men to predominate in fields imposing high quantitative demands, high physical risk, and low social demands, and the tendency of women to be drawn to less quantitatively demanding fields, safer jobs, and jobs with a higher social content are, at least in part, artifacts of an evolutionary history that has left the human species with a sexually dimorphic mind. These differences are proximately mediated by sex hormones. Copyright © 2006 John Wiley & Sons, Ltd.
In the fall of 1995, just seven of 95 students entering the undergraduate program in computer science at Carnegie Mellon University were women. In 2000, 54 of 130, or 42%, were women. What happened? This article presents a brief history of the transformation at Carnegie Mellon's School of Computer Science, and the research project that lay behind it. A fuller discussion, set in an analysis of gender issues in computing from childhood through college, is found in our book, Unlocking the Clubhouse: Women in Computing [2].The story begins with a research study designed specifically to diagnose and find remedies for the gender gap in Carnegie Mellon's undergraduate computer science program. Female enrollment had hovered below 10% for a number of years, and the fraction of women leaving the program was approximately twice that for men. In 1995, the Alfred P. Sloan Foundation funded our proposal for a two-year program, which was followed up two years later with a two-year extension. The goal was to understand the experiences and choices of both men and women with respect to studying computer science, and to design interventions that would involve more women.
At a cost to both their own opportunities and society's ability to produce people with much-needed technical skills, women continue to be underrepresented in computer science degree programs at both the undergraduate and graduate level. Although some of the barriers that women face have their foundations in cultural expectations established well before the college level, we believe that departments can take effective steps to increase recruitment and retention of women students. This paper describes several strategies we have adopted at Stanford over the past decade.
A general class of regression models for ordinal data is developed and discussed. These models utilize the ordinal nature of the data by describing various modes of stochastic ordering and this eliminates the need for assigning scores or otherwise assuming cardinality instead of ordinality. Two models in particular, the proportional odds and the proportional hazards models are likely to be most useful in practice because of the simplicity of their interpretation. These linear models are shown to be multivariate extensions of generalized linear models. Extensions to non‐linear models are discussed and it is shown that even here the method of iteratively reweighted least squares converges to the maximum likelihood estimate, a property which greatly simplifies the necessary computation. Applications are discussed with the aid of examples.
Although women have nearly attained equality with men in several formerly male-dominated fields, they remain underrepresented in the fields of science, technology, engineering, and mathematics (STEM). We argue that one important reason for this discrepancy is that STEM careers are perceived as less likely than careers in other fields to fulfill communal goals (e.g., working with or helping other people). Such perceptions might disproportionately affect women's career decisions, because women tend to endorse communal goals more than men. As predicted, we found that STEM careers, relative to other careers, were perceived to impede communal goals. Moreover, communal-goal endorsement negatively predicted interest in STEM careers, even when controlling for past experience and self-efficacy in science and mathematics. Understanding how communal goals influence people's interest in STEM fields thus provides a new perspective on the issue of women's representation in STEM careers.
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Theoretical models predict that overconfident investors trade excessively. We test this prediction by partitioning investors on gender. Psychological research demonstrates that, in areas such as finance, men are more overconfident than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997. We document that men trade 45 percent more than women. Trading reduces men's net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women. © 2000 the President and Fellows of Harvard College and the Massachusetts Institute of Technology
Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters. As heterogeneity measures, the random effects parameters included in the model are not easily interpreted. We discuss different alternative measures of heterogeneity and suggest using a median odds ratio measure that is a function of the original random effects parameters. The measure allows a simple interpretation, in terms of well-known odds ratios, that greatly facilitates communication between the data analyst and the subject-matter researcher. Three examples from different subject areas, mainly taken from our own experience, serve to motivate and illustrate different aspects of parameter interpretation in these models.
Reported is the 20-year follow-up of 1,975 mathematically gifted adolescents (top 1%) whose assessments at age 12 to 14 revealed robust gender differences in mathematical reasoning ability. Both sexes became exceptional achievers and perceived themselves as such; they reported uniformly high levels of degree attainment and satisfaction with both their career direction and their overall success. The earlier sex differences in mathematical reasoning ability did predict differential educational and occupational outcomes. The observed differences also appeared to be a function of sex differences in preferences for (a) inorganic versus organic disciplines and (b) a career-focused versus more-balanced life. Because profile differences in abilities and preferences are longitudinally stable, males probably will remain more represented in some disciplines, whereas females are likely to remain more represented in others. These data have policy implications for higher education and the world of work.