• Women in Computing As Problematic: A Summary

    I’ve long been interested in why, despite so much organized effort, there percentage of women in CS has been so stagnant. One hypothesis I had for some time was that the efforts themselves were unintentionally counter-productive: that they reinforced the gender subtyping of “female computer scientist” being separate from unmarked “computer scientists”.

    I was excited earlier this week when Siobhan Stevenson alerted me to this unpublished thesis from OISE: “Women in Computing as Problematic” by Susan Michele Sturman (2009).

    In 2005-6, Sturman conducted an institutional ethnography of the graduate CS programmes at two research-intensive universities in Ontario. In institutional ethnography, one starts by “reading up”: identifying those who have the least power and interviewing them about their everyday experiences. From what the interviews reveal, the researcher then goes on to interview those identified as having power over the initial participants.

    Interested in studying graduate-level computer science education, she started with female graduate students. This led her to the women in computing lunches and events, interviewing faculty members and administrators at those two universities. She also attended the Grace Hopper Celebration of Women in Computing (GHC) and analysed the texts and experiences she had there. Her goal was to understand the “women in computing” culture.

    In the style of science studies scholars like Bruno Latour, Sturman comes to the organized women in computing culture as an outsider. As a social scientist, she sees things differently: “Women in the field wonder what it is about women and women’s lives that keeps them from doing science, and feminists ask what it is about science that leads to social exclusion for women and other marginalized groups”

    Why I’m writing this summary

    Sadly, the thesis was never published. Sturman has since left academia and presently works as a high school teacher. I think the CS education community would benefit from hearing her findings. It’s worth noting upfront that Sturman is a poststructuralist: her goal is to problematize and deconstruct what she sees – not to test any hypothesis.

    The thesis is not an easy read. It’s a whopping 276-page read and took me about four hours. If you want to read it, but don’t want to read the whole thing, I suggest reading the last two chapters.

    I feel I only managed to get through it because I’ve taken courses from OISE on social theory. The thesis is extremely theoretical, and assumes the reader is fluent with the works of Dorothy Smith, Chandra Mohanty, Michel Foucault, Pierre Bourdieu, Stuart Hall, Bruno Latour, Thomas Kuhn, Evelyn Fox Keller, Sandra Acker, Judith Butler, Carol Gilligan, Donna Haraway, Judy Wajcman, and Simone de Beauvoir.

    Because the thesis has never been cited and it presents a very valuable perspective, I’m going to spend this blog post summarizing her findings. Where possible I’ll try to use Sturman’s own words, to foreground her own analysis.

    Women in Computing Lunches in the 80s

    The formal women in computing (WIC) lunches that Sturman attended have an interesting history. Female graduate students in the 80s felt the need “to come together for communication and support” amidst a “chilly climate” in their departments.

    For context, the 80s were a time when feminists were turning their attention to academia; women were organizing groups to draw attention to “women’s issues”, such as campus safety, sexual harassment, and workplace discrimination. These efforts were rooted in second wave feminism.

    The informal lunches and dinners in the 80s were “a student-initiated intervention to ‘help people in the program’”. As time went on, these female CS grad students became more activist. Some of these women went on to “present a report to the university about improving the ‘climate’ for women in science. Among the local concerns for women in Computer Science at the university was building at campus safety at night when they worked late in labs”.

    Women in Computing Lunches in the 00s

    Sturman spends a fair bit of time contrasting the lunches from the 80s with their form as she saw them in 2005-06. These lunches were now formal gatherings, organized by computer science departments. They had become institution-led rather than student-led, with a goal of keeping women enrolled in CS.

    The faculty who organized the lunches described the goal as “to create community …. [she] recalled her own experience as a graduate student when the few women in Computer Science and Engineering bonded informally in order to actively improve the chilly climate for women in their departments (Prentice, 2000). However, the institutional creation of ‘community’ sets formal boundaries that both constraint [sic] and enable practice.”

    Many of the graduate students in the 00s felt they had “nothing in common” with the other women at these lunches: “there was no inherent commonality to the group based on gender, other than the fact that they were all women, and all together ‘in the same room’. …. The diversity of their countries of origin, their educational backgrounds, their family responsibilities (or not) and many other aspects of their lives often made the idea of shared ‘experience’ seem alien to them.’

    The concerns of the women at these lunches was also different than in the 80s: discussion focused on individual choices and satisfaction, rather than workplace conditions. Rhetoric at the lunches was consistent with what we’d now call “Lean In” culture: trying to improve the psychology of the women there (promote yourself! etc) than make structural change.

    Ironically, these lunches “often had a negative effect upon [the female grad students’] self-perceptions and upon their hopes to help implement change” – the lunches did not “encourage graduate student participation in any comprehensive plan to change the male-dominate ‘culture of computing’ […] Rather, it seemed to students that the existence of the group and the funding of ‘get-togethers’ served as the main commitment of the department (and of university administrative resources) to gender equity, though both universities were concerned abut the declining enrolment of both male and female students in undergraduate Computer Science.”

    The female grad students in the 00s who did want to make change at their institutions instead found other avenues, such as undergrad affairs committees, or labour unions, more productive than the WIC lunches. This work, and that of the female faculty running the WIC lunches, was seen sometimes a burden by the women doing it. It took time and emotional energy for which they were not compensated. Many of the female students noted that they had to be careful how much work they did promoting women in CS; if they were seen as doing “too much” they anticipated it would hurt their job prospects.

    As CS departments embraced numerical-based goals for diversity, the female graduate students felt like they were used by their institutions to meet these institutional diversity goals: “To S and some of the other students … the ‘women in computing’ groups seemed a position of powerlessness. In such a position, they felt subjectified as ‘women in computing’ for special interest from the university but with little direct power in effecting institution change where gender inequity was identified”.

    As for the community building, one participant stated that “I don’t really feel a huge need for a sort of a… support group”. The lunches were seen more as a networking opportunity. Some women “disidentified with the problems other women talked about, and were depressed by or disinterested in warnings about ‘toxins’ that they as yet did not detect”. As one participant put it, “the stories [at the lunches] will just depress me [if I go].”

    The young women were often ambivalent about these lunches and other WIC initiatives, feeling uncomfortable about receiving “special treatment”. One participant worried it would seem “‘unfair’ to her male colleagues”. Other women described receiving “special treatment” in as undermining their identities as independent and self-made computer scientists.

    Women’s Work in Computing

    Participating in women’s lunches and presenting research at women’s conferences contrasted with the “gender-neutral” activities that women in CS must also partake in. Women felt pressure both to go to ordinary (male-dominated yet “gender-netural”) conferences as well as to present research at the women’s conferences.

    As Sturman put it: “for the women graduate students in Computer Science who participated in this study, the university practice of ‘gender equity’ means negotiating the contradictory position of attending to institutional demands for individuated ‘gender neutral’ scholarly performance in competition with peers for external and internal awards, jobs and research recognition while at the same time being hailed to affiliate with the ‘community’ of ‘women in computing’. Through these intersecting discourses, for many of these students, the sign of ‘feminism’ seems more a set of institutional rules and boundaries for gender performance and identity management than a relevant activist project.”

    At the same time, Sturman observed that certain subfields and activities in CS have become seen as “female-friendly” because they are seen as having more social relevancy and collaborative work. These areas include software engineering, human-computer interaction, computational linguistics, as well as the practice of teaching. Sturman also noted a perception of these areas being less mathematical, reflecting gender stereotypes that women don’t like math.

    Sturman challenges the categorization of these areas as “female-friendly” – the assumption that social relevancy and collaboration (and less math) are suitable for women reflects outdated and harmful stereotypes of women.

    The institutional culture at both universities Sturman studied valued the more masculine, abstract subfields of CS. Areas like SE and HCI were not seen as “real” computer science and given less respect. Teaching-track faculty were not seen as doing as contributing equally to the department as research faculty. Indeed, teaching track jobs were described as more suitable for women so they wouldn’t have to be burdened by research and “you look after your children”.

    Work/Life Balance as Problematic

    The discourse of “work-life balance” appears frequently in the women in computing arena. Sturman viewed this in terms of gender performance: “[a female grad student being interviewed talked] as she showed me pictures of herself and her boyfriend on a camping trip as evidence of her successful performance of ‘work-life balance’. The successful management of ‘work-life balance’ is a workshop topic at many ‘women in computing’ events. In contradictory interaction with the intensification of institutional performance of the self, it is often an almost impossible feat for young women (and men) who are entering the CS/IT workforce, whether in the academy or in industry. Proof of the accomplishment of work-life balance, or of attempts to self-improve in that direction, is an important part of ‘women in computing’ discourse.”

    Furthermore: “The illusory goal of work/life balance … moves ‘women in computing’ discourse away from political activity for better material working conditions to an individualized psychology-based call to improve behaviour and attitudes (both personal and institutional). … by embracing ‘work/life balance’, which reinscribes heteronormative “family life” […] [and] stands in binary gendered relation to an undesired identification with the work-obsessed male ‘nerd’ who has ‘no life’.”

    “This discourse plays in contradictory relation to the discourse of ambition, which implies that women need to improve themselves by being more attentive to public recognition and career advancement. Demands for the recognition of work/life balance issues are considered in the dominant male CS culture as a ‘personal choice’ to take a less committed (and therefore less materially compensated) role at work; this is also true for men in CS who want to take a more active role in parenting. The self-improvement discourse of ‘women in computing’ posits work/life balance as yet another performance indicator, suggesting that women should strive for high performance goals in both work and family life. This discourse seemed a contradictory and exhausting path for many of the women faculty members I interviewed, who balanced the demanding research performance that was expected of them at their universities with a heavy teaching and service workload and, in some cases, young children as well.”

    Sturman then spends some time analyzing the recommendations from CRA-W for faculty interested in increasing female participation in their classes, identifying how these texts focus “on the individualized promotion of ‘choice’ and ‘career satisfaction’ away from any analysis of a need for structural change.”

    Marketing the “Woman in Computer Science”

    Another theme of WIC events that Sturman observed was workshops and advice on how to market one’s self as a woman in computer science. Here, “an aestheticized anti-geek self-identity is encouraged as a marketable commodity for women seeking CS/IT careers”.

    In trying to combat the stereotype of computer scientists as male geeks, WIC culture presents women in computing as hyperfeminine. These idealized women are young, attractive, have “soft skills”, and also can balance work and life. Sturman spends some time analyzing the promotional material for Grace Hopper. These posters (at the time of analysis) always showed young pretty women, usually white. The posters never show the “old faces” of computing: white men and Asian women and men.

    The frequent suggestions that young women need “role models” serves to communicate to young women what kind of woman they should become in order to be a woman in computing. The graduate students that Sturman interviewed reported having to carefully present their identities, to try to further their careers.

    While at Grace Hopper, Sturman observed how much the celebration has become a place for tech companies to recruit women. Many of the women recruited to these tech companies are later paced into non-technical managerial positions.

    One session at Grace Hopper that Sturman attended was entitled “Embrace your duality as an Asian woman to lead” and its abstract adversities “We will brainstorm how to leverage our gender strength to excel, and to embrace our cultural advantage to lead!” At the session, Asian women were taught to treat their identity as a commodity. Universities are seeking to attract more “international students” and companies more “migrant Asian IT worker” – labels which serve to treat these women as outsiders, rather than North Americans.

    Networking at Grace Hopper

    Sturman’s research brought her to Grace Hopper. Her first impressions are clear: a focus on “the contested concept of numeric equality” in CS, and how the choice of naming the conference after a military scientist “provides a backdrop to the many intersecting contradictions in this milieu, where academic, corporate and military interests converge to produce the organizing texts forming the category of ‘women in computing’ as a target group for inclusion, marketing and co-optation.”

    She finds a paradox at the celebration: the “North American Second Wave liberal feminist belief in ‘universal sisterhood’” is present at the same time as neoliberal political and economic practices. Neoliberal political/economics refer to the decentralization of the State and the decollectivization of the workplace – or as their proponents would put it, making the workforce more “flexible”, and workers more “independent”.

    For the female students who attended, the main pull was networking with other women in the field, and with the large tech companies who recruit there. The older generation’s goals of universal sisterhood were not shared with the young: “there was little or no identification with networking as a method of establishing solidarity or group affiliation as ‘women’; most understood the practice as instrumental in the establishment of professional contacts, but little more”.

    Personally, I’ve had people recommend that I attend Grace Hopper so that I can “network with the Ol’ Gal’s Club” – the conference has established a network of high-achieving women to rival the Ol’ Boys Club of computer science, rather than dismantle the club-based system.

    Female Friendly Education?

    A particular session stood out for Sturman, titled “‘Female Friendly Education: Increasing Participation or Watering Down?’ provoked a heated response from audience members, many of whom were university students and professors. The topic unexpectedly made clear how the insertion of diverse women into a generalized female identity in ‘women in computing’ or ‘female friendly Computer Science’ is extremely problematic, constituting a unitary subject which also produces those it does not include as marginal. Among the panelists, Sue Rosser, a feminist scholar of science at Georgia Tech, had coined the term ‘female friendly science’ (1990) to signify a new approach to university science education which would take into account how science is gendered and would seek to redirect values and practices in scientific work to reflect the interests and experiences of women as well as men.”

    “Certainly, in the group of female graduate CS students I interviewed, the majority resisted the characteristics ascribed to them in a ‘female friendly’ Computer Science approach, characteristics which positioned them as gendered subjects, read as less competent and less able in relation to the dominant unmarked male subject of Computer Science. The panelists presented their questions to the audience in an attempt to get feedback about why ‘third wave’ (their term) feminists and younger CS women in general responded negatively to the term ‘female friendly…”

    “The practices of ‘female friendly’ science assemble a subject which is the Other, as the term ‘female friendly science’ itself acknowledges that the discipline of science limits the right of women to participate in its laws of discourse. Thus ‘female friendly science’ performs a reinscription of social exclusion. The existing forms of scientific discourse, or what the presenters described as masculinized STEM values, ‘an emphasis on truth, beauty and puzzles’, determine the limits by which femininized values of ‘time spent on community and social impact’ can be uttered as a ‘truth’ about women through the gender binary.”

    Retraditionalizing the Gender Binary

    Indeed, much of the rhetoric Sturman observed at Grace Hopper served to reinforce the gender binary. “Throughout the conference, there was a lot of talk about countering the geek image, supported by print and visual media; essentialized female identity appeared to shut out those who didn’t conform.”

    Much of the motivation for recruiting more women in to CS “reflected concern that young women will be ‘left behind’ because they are not pursuing Computer Science or IT as career paths; this is produced through a discourse of fear that America will be left behind in the face of global IT competition, particularly that coming from Asia. In the media representations at Grace Hopper, young women at the conference are heavily encouraged to be leaders in the field, icons for innovation, change, diversity, flexibility and collaboration; this supports an essentialized cataloging of women’s ‘inherent’ strengths in working with people and across difference.”

    “A series of keynote speakers modeled different aspects of computer science and information technology in the academy, in industry and in the U.S. Space program. This ‘intentional role modeling’ is seen by many promoters of women’s participation in IT as “a way to overcome the negative effects of stereotypes by increasing self-ratings and by inspiring and motivating achievement”. … Once again, this places young women in the dual role of being both strong and ‘at risk’ (Harris, 2001), strong in that their participation in IT work drives the national economy, but at risk of not living up to the expectations set by the role model, and potentially failing their own expectations for a high-paying and meaningful career. These intentional role models, as living motivational texts, organize work for those with already high self-expectations … and to push them to achieve further as selfactuating, self-(im)proving subjects. Not only is their own self-worth dependent on their achievement, but, they are told, so is the success of the nation: “Women’s lack of participation in IT has deep implications for our country’s preparedness, competitiveness, economic wellbeing, and quality of life” (NCWIT, n.d.).” (emphasis mine)

    The Merit vs. Equality Debate

    Noticeably, much of the policies and texts surrounding computer science education in Ontario place an emphasis on producing IT workers. “In much of the discussion around inclusion strategies in CS/IT education, the exchange often takes the form of a debate between those who point to the underrepresentation of women and targetted minority groups as not only inequitable but also potentially limiting to the development of future North American CS/IT workers, and those who argue against a ‘watering down’ of Computer Science as an academic discipline for the sake of filling enrolment targets.”

    Sturman is far from the first to observe the merit vs. equality debate in computer science. She presents some examples of texts representing these discourses. Since many others have covered this topic, I’ll be brief here.

    She draws a connection to the focus of numerical equality: that “dominant belief for practitioners is that getting more women into the field will change science; Schiebinger (2000) suggests that only feminist analysis alongside women’s greater participation within the field will bring that change about, as past history and current resistance to change indicates.”


    If it wasn’t clear by now, Sturman is not a positivist. Indeed, she argues that the hegemony of positivism in computer science is part of the problem: it leads to a focus on what is measurable and “objective” (ie. numeric equality) rather than on the “subjective” experiences and emotions of the women in CS.

    Positivism also falls prey to the grand narrative of cause and effect. She describes much of the research on women in CS: “The general purpose of these investigations seems to be to identify a cause-effect pathway and then to proceed with an equally linear solution. This strategy mirrors the methods of positivist science, and the reasoning is that a solution to this scientific/technical problem must use parallel scientific means … “The difficulty with this approach is that the subject of science is transparent to itself and tends to reproduce its own image iteratively. Thus the ‘problem’ of ‘women in computing’ bounces back to researchers without much in the way of new insights, let alone solutions. The ‘numbers game’ dominates, even as feminist proponents of gender equity in Science, Technology, Engineering and Mathematics (STEM) acknowledge its failure.” “

    Publishing practices also contribute to the “problem”: she talks to people who tried to publish “failed” interventions and were rejected. “Gender” is seen as a niche issue: “she was told that the journal had already published a lot of submissions on ‘gender’”

    It’s my opinion that the CS education scene has improved on these fronts since 2006 (there are good places like ICER and RESPECT that have been created, but they are not representative of all CS education). But women in computing groups today still seem uncomfortable with non-positivist research. I attended Can-CWiC earlier this year and noticed a huge divide between the female faculty (who wanted to hear about my research and were excited by it) and the female students (who were dismissive of me for not being a real “computer scientist”).

    A Final Quote from the Thesis

    One of Sturman’s faculty participants candidly noted that “…to keep in mind … is we’re the survivors. We’re the ones who could put up with all the crap, and we had enough, a strong enough sense of self, or our personalities are such that we fit in well enough with the dominant, mostly male environment that we were able to do fine. So we would be the last ones who’d be able to tell that the atmosphere is poor… in some sense we’re part of it, if that makes sense [Laughs]. We were selected for being able to put up with toxins, so we’re probably not the best ones to detect it, necessarily”

    My Own Thoughts

    My first reactions to the thesis were excitement that somebody has done this work – only to follow with disappointment that the work was never published, and never disseminated to the audience it would most benefit: computer scientists.

    Not only was it never disseminated, it’s written in a way that is really only accessible to theory-oriented sociologists. She describes almost nothing of her methods or analytic process. No primer is given on the many social theories that she uses.

    While poststructuralism doesn’t aim to verify hypotheses (like positivism) nor to explain the world (like constructivism), it does aim to provide new narratives to counteract problematic ones. An unpublished, inaccessible thesis does not help computer scientists rethink their problematic narratives and build new ones.

    Since Sturman did her research in 2005, some things have changed. Some of her critiques and observations have been independently made. For example, Sarah Nadav’s post that “The “Women in Tech” movement is full of victim blaming bullshit” recently went viral. And within the CS Ed community, I’ve spent some time at ICER critiquing women in CS initiatives for many of the same reasons, though based more on evidence from social psychology. I’ve also written on the generational differences between the women running Women in Computing events and the young women attending them (see here and here). I’m wishing I’d read her thesis three years ago!

    There are things that have been deconstructed since 2005 which Sturman didn’t pick up on. One is the pipeline discourse, which Sturman treats unproblematically. Another is her own, and her participants’ use of “females” as a noun, which is a personal pet peeve of mine.

    I found Sturman’s discussion of Unlocking the Clubhouse as simplistic: while she made the key observation that the initiatives had the political support to succeed, she views the curricular changes as the only ones. The folks I’ve talked to about CMU’s initiatives have indicated that it was the admissions changes that may have been the most significant.

    Since Sturman wrote her thesis, many scholars have critiqued how “women in STEM” is being presented. Marieke van den Brink, for example, has found many of the same discourses and paradoxes in the women in physics community. Padavic et al have pushed on the “work/life discourse” as problematic with interesting insights. Overall, the focus has been slowly shifting from trying to “fix” the women/girls to fixing the structure of science. Sturman’s thesis gives us more ammunition for arguing that the old way of promoting “women in computing” is problematic and in need of rethinking.

  • Getting Fedora 23 working on an Asus Zenbook UX305CA (Intel Skylake)


    I recently acquired a shiny new Asus Zenbook UX305CA to replace my old UX32A which had been dying a slow death for the past year.

    Excitedly, I put the latest Fedora release (23) on the computer, using the Cinnamon spin. While the computer ran Fedora, the screen resolution was set at 800x600 with no other options.

    The issue? The Intel Skylake chip in the computer wasn’t supported by the kernel that Fedora 23 ships with (kernel version 2.3). Like many linux users with new laptops I’ve found myself in a bit of an adventure with the new skylake chip. I thought I’d write up how I eventually got Fedora 23 working on this computer for the sake of those following the same path.

    To get linux working with kernel 2.3, I found the Arch Wiki invaluable:

    • I needed the kernel boot argument: i915.preliminary_hw_support=1
    • And then you set xorg.conf as described in the Arch Wiki
      Once both of those were done my computer was working, but without hardware acceleration. The next step was to install kernel 4.4, which supports Skylake.

    • You’ll want to add the repository where Fedora keeps the latest kernel versions: I found 4.4 in kernel-vanilla-stable (see instructions here)

    • Then, once I tried booting with kernel-4.4, I got an error at boot: “double free at 0x(address) Aborted. Press any key to exit“. To get rid of the error, I found I had to temporarily disable the validation steps of the new kernel as described in comment 18 on the bugzilla report
    • The mokutil utility will ask you to set a password for altering safe boot. Write it down. When you reboot it will ask for the password on a character by character basis, where the order of the characters is random. I wound up failing this the first time because I assumed the password should be 0-indexed; it’s actually 1-indexed.
    • Once I had insecure boot turned on, I could successfully boot kernel-4.4! But cinnamon informed me that software rendering was still on. To solve this, I had to undo what I’d done to make kernel-4.2 work: take out the i915.preliminary_hw_support=1 and set xorg.conf to what is recommended for Intel graphics in general rather than the Skylake bandaid (you just take out the options line).
      Once all that was done, the computer’s working quite nicely!
  • On Paulo Freire, and seeing computing as literacy

    Paulo Freire was a Brazilian educator, best known for his book Pedagogy of the Oppressed. Indeed, it’s the most commonly assigned reading in education classes which isn’t a textbook. His ideas have been used for teaching many topics, such as health and African American studies. And yet, most people in CS education circles aren’t familiar with Freire. In this post I’ll provide a short introduction to Freire and why his work is relevant to computing education.

    To Freire, education is an inherently political act. Education can be a tool of empowerment, and it can also be a tool of oppression. Freire refered to traditional education as the “banking model”: the teacher deposits coins of knowledge into the bank accounts of the students. _”Instead of communicating, the teacher issues communiques and makes deposits which the students patiently receive, memorize, and repeat. This is the “banking” concept of education, in which the scope of action allowed to students extends only as far as receiving, filing, and storing the deposits.” _(Freire, 1968)

    This model ignores what the student already may know. It fails to give the students a sense of ownership over their knowledge, and fails to stimulate critical thinking. He argued this reinforces oppression. For education to be empowering, students need to be active agents in their own learning.

    Problem Posing Education

    As an educator, Freire focused on literacy education. He and his colleagues ran community-based literacy education projects for Brazil’s poor, focusing on adult education. He championed what he called problem-posing education, which focuses on “listening, dialogue, and action”.

    As his teams implemented education, they first spent time observing what their students do in their everyday. What written words the students encountered but could not read. What texts the members of the community would observe. They listened, observed, and tailored their curricula around what the people of that community would benefit from learning.

    Teaching was done as a dialogue. Not just in a Socratic fashion, but also as sharing between teacher and student. To Freire, _”authentic education is not carried on by “A” for “B” or by “A” about “B,” but rather by “A” with “B.”” (Indeed, _one of the reasons I enjoy teaching is how much I learn from my students.)

    Education didn’t end with the lesson: the goal of teaching is to empower the learner to action. His students could now read the news and understand local politics. They could now vote in a society where literacy was required for suffrage. They could now organize and advocate for their own interests. Freire didn’t consider his job done unless his students had acted using what they had learnt.

    Computing as Literacy

    The view of computing as being like literacy has recently caught on. After all, computing is a sort of language, and it unlocks a level of literacy in society. Arguing that computational thinking is a form of literacy can be persuasive in policy circles.

    I don’t think the parallel between literacy and computing should end there. For example, Sally Fincher recently wrote a CACM piece drawing parallels on literacy education and computing education, which I think was quite insightful. We can learn from the lessons of literacy education.

    I think that like literacy, computing is incredibly empowering. In the age of the the computer, those of us with computer science knowledge have power that others do not.

    Our society has come to accept that universal literacy should be a right, and that everybody is capable of basic literacy. CS educators still aren’t there yet: there are still plenty who believe not everybody has the “Geek Gene” to understand computing.

    People used to think similar things about literacy. But as literacy education improved its methods, and learnt how to diagnose and teach people with disabilities, it became accepted that anybody can learn to read. Mark Guzdial has argued that the reason the Geek Gene hypothesis is so popular is because we fundamentally don’t understand how to teach computer science.

    Learning from Freire

    Most of the efforts trying to view computing as literacy have been focusing on k-12 education, trying to inculcate a new generation. A Freirean approach would instead focus on adult education: we can’t leave adults behind. And if we teach the parents, the parents can teach the children.

    There’s some evidence that girls realize they don’t “belong” at the computer from watching their mothers’ lack of confidence at the computer (Margolis and Fisher, 2002). Teaching parents means teaching children.

    Talking to people trying to get CS into k-12, often the barriers are a lack of adult education. Teachers and principals don’t know about CS. Deans of education schools don’t know about CS.

    Sometimes I worry our goals in computing education are too often determined by industry. Industry wants young people to learn to code, so they grow up to work in industry. Teaching adults less clearly benefits industry. It does benefit democratic society. We need adults to be well informed of computational issues as legislators draft laws affecting internet privacy and security. 

    I think end-user computer science education can learn a lot from Freire. I’ve long taken an interest in teaching scientists to program. Problem-posing education strikes me as incredibly useful here: first, we as educators observe how scientists do their work. What computational problems do they encounter (and fail to solve)? We then tailor a curriculum around listening to our audience. Then we share it with our students, using active and/or participatory learning techniques, having a dialogue_ _with our students. Finally, we empower our students to go out and act: they can solve their scientific problems with code.

    Mark Guzdial recently wrote a book about teaching CS to everybody, which I recommend. He describes different curricula tailored for different audiences, and promotes active learning methods. My one complaint is he never cites Freire! (Since it’s a literature review of the CS education literature, and the CS education literature doesn’t cite Freire, this is to be expected.)

    Different communities in our society will have different needs for computing education. We as educators should listen to their different needs, dialogue with them, and empower them to action. Too often we use the banking model to teach computer science: we impose a curriculum on our learners without regard for whey would benefit from learning or for empowering our students.

    If we want universal computer science education, the banking model will fail us: there is no single perfect curriculum, and mere transmission of knowledge will not foster empowerment nor critical thinking. And while there’s validity in the  economic arguments for teaching everybody to code, it’s to our detriment as educators to forget the socio-political part of our work.


    Freire, P. 1968. Pedagogy of the Oppressed.
    Margolis, J. and Fisher, A. 2002. Unlocking the Clubhouse.

  • Impostor syndrome viewed through the lens of social theory


    Sociologists like to use performance as a metaphor for everyday life. Erving Goffman in particular championed the metaphor, bringing to light how our social interactions take place on various stages according to various scripts. And when people don’t follow the right script on the right stage, social punishment ensues (e.g. stigma).

    Pierre Bourdieu rather similarly described social interactions as taking place in arenas, seeing them more like games than plays. (Sometimes champs is translated as ‘field’ rather than arena; it’s worth noting Bourdieu intended for it to have a connation of sport/war.) Rather than a script, people get a sense for the rules of the game. And when people don’t follow the rules of the game, social punishment ensues.

    Whether one is failing at a social game or performance, social punishment can take many forms. For example, sexual harassment is most reported by those who go against gender roles. Powerful women are more likely to be harassed than less powerful women. Women in male-dominated fields are more likely to be harassed. Men who are effeminate, gay, or champions of feminism, are more likely to be harassed. Harassers act to keep people “in their place”.

    Since not following the script/game is costly for individuals, we’re trained from a young age to be on the lookout for cues about what stage/arena we’re on and what role we should be playing. Looking for and responding to cues is something we do automatically most of the time. Kahneman would see it as an example of System 1 thinking.

    Impostor syndrome is the sense that you’re the wrong person to be playing the role you’re in. You’re acting a role that you’ve been trained in and hired for – but your brain is picking up on cues that signal that you’re not right for the role.

    The Caltech Counselling Centre has this to say on “who is likely to have the impostor syndrome?“:

    Attitudes, beliefs, direct or indirect messages that we received from our parents or from other significant people in our lives early on may have contributed to the development of impostor feelings. Certain family situations and dynamics tend to contribute to impostor feelings: when the success and career aspirations conflicts with the family expectations of the gender, race, religion, or age of the person, families who impose unrealistic standards, families who are very critical, and families who are ridden with conflict and anger.

    Some researchers identify two main types of family dynamics that can contribute to impostor feelings, although there may be others.

    Family Labels:  Different children in a family may be identified or labeled differently.  For example, some families have one “intelligent” child and one “sensitive” child.  While growing up, many times families will not change their perception of each child, no matter what that child does.  Therefore, the sensitive child, even if she gets better grades or more awards may not be recognized for her intelligence.  This can lead to doubting her intelligence and believing the family is correct even with evidence, which contradicts these labels. 
    The sensitive child in this example has been raised to play the script of the sensitive child. When they go on to play other roles, they still sometimes encounter social cues indicating they’re in the wrong role. Impostor syndrome results.

    Impostor syndrome is thought to be quite common amongst women in science. In this light I don’t think it’s surprising: there are so many cues in society that we are not what a ‘scientist’ is supposed to look or act like. We don’t fit the stereotypes. Many female scientists were raised to be that sensitive child.

    I started reading about impostor syndrome when I was asked if I had any ideas on whether Impostor Syndrome is a recent phenomenon in society. The syndrome was first termed in the 70s, but sociologists and psychologists had described similar things well before then.

    I would expect its prevalence is a relatively recent phenomenon. In “the good old days” people had extremely rigid options for what roles they could have in society. Women had few if any career options. There was little social mobility. Non-white people had even less social mobility. Most people followed a career trajectory sculpted by their parents – not by themselves. And so, people had a script determined for them. Relatively few people had the ability to deviate from it successfully. They could only play roles that had been assigned to them.

    In modern society, most of us have the privilege of picking the roles we want to play. Regardless of whether we were raised to fit the role, or look like the stereotype. I don’t think people with impostor syndrome are crazy:_ I think they’re picking up on cues that they’re not in a role they were created to be in_.

    Reflecting on the times I’ve experienced impostor syndrome, they were situations where I didn’t look the part (too young, too female). Or they were situations that I hadn’t been raised to fit into – I was raised to be nerdy/geeky. I feel like an impostor at the gym, and I definitely felt like an impostor when I taught a fencing class many years ago.

    I don’t have a magic answer for getting over impostor syndrome, and the link between social cues and impostor syndrome stands only as a hypothesis at this point. But I do think we impostors are necessary to subvert social scripts. Just because you don’t look the stereotype or were raised to do doesn’t mean you can’t, and hopefully that won’t stop you.


    Berdahl, J. 2013. Testimony on Sexual Harassment to the Canadian House of Commons Standing Committee on the Status of Women.
    Bourdieu, P. 1979. La distinction.
    Caltech Counselling Center. The Impostor Syndrome.
    Goffman, E. 1959. The Presentation of Self in Everyday Life.
    Kahneman, D. 2011. Thinking Fast and Slow.

  • A brief introduction to social theory


    Theories from psychology enjoy a fair bit of use in computer science education, but education is not merely a cognitive process: it’s also a social one.

    I’ve found it useful to learn about social theory as a CS education graduate student, and I thought I’d share a quick introduction to social theory that I initially wrote for my research proposal to my thesis committee this fall.

    Classical Social Theory

    Classically, sociology has had four major schools of thought, each of which goes by various names and is associated with one of the four “founders” of sociology:

    1. Auguste Comte (1798-1857) coined the term “positive philosophy”, now better known as positivism. Comte’s sociology was inspired by the French Revolution: sociology was envisioned as a means to produce the perfect society. (Indeed, Comte was incensed that the lower classes wouldn’t simply accept their “place” in society.)
      In Comte’s world, one would test out different ideas for how to run a society, and find the optimal approach. While Comte himself argued for holism, (post)positivism has since come to be associated with reductionism.

    2. The sociology of Max Weber (1864-1920) contrasts with Comte’s: Weber was a proponent of anti-positivism (also known as constructivism or interpretivism). Weber saw verstehen (understanding) as the goal of research, rather than hypothesis verification. Weber theorized upon social stratification; he also wrote about closure (how groups draw the boundaries and construct identities, and compete with out-group members for scarce resources.)

      Weber’s sociology put an emphasis on ideology. Capitalism, for example, was the result of ideological conditions unique to Northern Europe; capitalism has only succeeded where these ideological conditions hold.

    3. Emile Durkheim (1858-1917) built on Comte’s positivism, setting forth structural functionalism. In structural functionalism, a society is viewed like a biological cell: different parts of a society are likened organelles. Durkheim’s sociology looks at how the parts work together to comprise the whole. It is also holistic – much of how systems thinking was used in the social sciences built on Durkheimian notions of society.

    4. Finally, Karl Marx (1818-1883) provided an approach which contrasts with Durkheim’s: instead of seeing harmony, it emphasizes the role of class conflict in society and the historical-economic basis thereof. Marxist sociology has also been known as conflict theory, though the historical-economic basis is not the only way one could study conflict. For example, Weberians see conflict rooted in ideology, rather than in a clash over resources.

    20th Century Social Theory

    While classical theory is often referred to in terms of thinkers (Marx, Weber, etc), the more modern movements tend to be known more by schools of thought. Some of the major ones would be:

    1. Neo-Marxism refers to the 20th century updates of Marxist theory, which has pulled in Weberian and poststructuralist work on status and power. Antonio Gramsci is a well-known neo-Marxist, who was curious about the question of why the revolution Marx had predicted never seemed to come about. Gramsci is most famous for his theory of cultural hegemony.

      Critical theory is also based on Marxist thought, and is often conflated with it. Critical theory emphasizes praxis, the combination of theory and practice. Critical theory is most associated with the Frankfurt School, including names such as Theodor Adorno and Jurgen Habermas.

    2. Interactionism assumes that all social processes are the result of human interaction. It emerged in the early 20th century. Interactionaists focus their studies on the interactions between individuals. As a result, interactionists do not `see’ the effects of physical environment – or even solitary thought/work. They also reject quantitative data in favour of qualitative approaches: grounded theory and ethnomethodology were both developed by interactionists. The notion of social interaction as a performance was first developed in interactionist thought; poststructuralists have since refined it. While there’s no single name associated with this perspective, some associated names include George Herbert Mead, Erving Goffman, and Dorothy Smith.
      Sociologists also look at social systems at different levels: the macro level looks at entire societies, nations, etc; the meso level looks at organizations, institutions, etc; the micro level looks at individuals. While classical sociology was generally macro, interactionism focuses on the micro level.

    3. Structuralism might be seen as a macro-focused backlash against interactionism’s focus on the micro. Structuralism sees social processes as stemming from larger, overarching structures, and also emerged in the early 20th century. Structuralists see society as being governed by these structures in a somewhat analogous fashion to how physicists may see the universe as being governed by laws of nature. A criticism of structuralism is that it sees these structures as fixed; in contrast a Marxist would focus on historical change. Some structuralists include Claude Levi-Strauss, Ferdinand de Saussure, and Jean Piaget.

    4. Poststructuralism (more or less interchangeable with “postmodernism”) is not a particularly coherent school of thought. This is not altogether surprising as the key poststructuralists both reject the label and the very notion that there is such a real thing as poststructuralism. Poststructuralists reject the idea of “objective” knowledge: since the study of sociology is done by humans who are biased by history and culture, they argue that any study of a social phenomenon must be combined with how the study of that social phenomenon was produced. For example, a poststructuralist would not take a concept like `gender’ as a given, but problematize the concept. Poststructuralism evolved out of structuralism in the mid 20th century. Some major poststructuralists include Michel Foucault, Jacques Derrida, and Judith Butler.
  • A Survey of High School CS in Canada


    I thought I’d have a look at the status of high school CS across Canada. (I’m keeping this to provinces/territories that have a population greater than 500,000.)

    Overall, it’s generally categorized as an elective, often lumped in with fine arts and second languages. BC, Alberta and Manitoba are the only provinces where it is categorized as a teachable subject at the BEd level; in Ontario it is available as a minor. (Quebec, as usual, is a beast of its own not easily comparable to the other provinces.)

    Western Canada seems to be leading the pack for standards and teacher support – the only CSTA chapters in Canada are in BC, Alberta, Saskatchewan and Manitoba.

    Atlantic Canada is furthest behind: New Brunswick, Nova Scotia and Newfoundland and Labrador do not appear to have computer science curricula, let alone CS teacher training/support.

    Nowhere easily available on the internet could I find stats on how many schools teach CS or how many students take it – this information is FOIable if anybody really wants to see the numbers. (If you do have the numbers, I’d love to see them!)

    British Columbia:

    • British Columbia does have a CS curriculum. It’s lumped in with “computer studies” that is mostly IT. It is categorized as “Applied Skills“ rather than a Science or Math.
    • CS is not categorized as required course for a high school diploma, but can be counted as an elective topic. BC students need to take 28 credits of elective topics.
    • The University of British Columbia offers a BEd in computer science, as does the University of Victoria and the University of Northern BC. I can’t find any BSc/BEd combined programmes for CS.
    • There is a CSTA chapter for BC.
    • I can’t find any stats on how many BC schools teach CS. My impression is that it’s not uncommon in Vancouver but rare elsewhere.

    • The Albertan universities (University of Alberta, U of Calgary, U of Lethbridge) accept high school computer science as science credit for entry to their science programmes

    • High school CS counts for graduation requirements as a elective, not as science or math. This said, students need to take 10 credits from the elective list (CS, fine arts, apprenticeships, PE, and second languages.) There are multiple CS courses on the books in Alberta’s education system.
    • The University of Alberta offers a BEd in computer science as well as a combined BSc/BEd programme in CS. The Universities of Calgary and Lethbridge do not offer BEd programmes in CS.
    • There is a CSTA chapter for Alberta.
    • I can’t find any stats on how many schools offer CS. The high school I attended in Lethbridge did not offer CS when I was a student (I was class of 2007) and I don’t know anybody from Alberta who took high school CS.

    • Saskatchewan does have a high school CS curriculum (CS 20/30). It is not required for a high school diploma. It appears that CS 20 can count as a science elective, but not CS 30?

    • Computer Science 20 is mentioned in some Saskatchewan Education documents but not CS 30 – I’m guessing CS 30 is not widely taught?* Computer Science 30 counts for science credit when applying to the University of Regina but not at the University of Saskatchewan.
    • Neither U of Saskatchewan nor Regina offer BEd programmes in CS.* There is a CSTA chapter for Saskatchewan.
    • I can’t find any stats on how many schools offer CS.

    • CS is not required for high school graduation – or even listed in the electives section. There is a high school CS curriculum.

    • The University of Manitoba has a BEd in CS, but not the University of Winnipeg.* There is a CSTA chapter for Manitoba. 
    • I can’t find any stats on how many schools offer CS.

    • CS is not required for high school graduation. Students do need to take one credit from science, computer studies, IT, and French. There is a high school CS curriculum.

    • Computer science is not available to as a major in BEd programmes, but only as a minor.
    • There is no CSTA chapter for Ontario. And as usual, I can’t find any stats on how many schools offer CS.

    • Quebec has a very different education system from the rest of Canada: students complete a secondary diploma in grade 11 then go to CEGEP for a couple years before going to university. CEGEP covers the equivalent of first year university. I’m hesitant to compare Quebec’s education system to the other provinces as a result.

    • Computer and technology studies count as a science for secondary diploma purposes. I can’t find a CS curriculum for the secondary level, however.
    • Computer science (informatique) appears available at all the CEGEPs in Quebec in some form or another.
    • There is no CSTA chapter, nor clearly available stats.
      New Brunswick:

    • Computer Science is categorized as in “Skilled Trades and Technology Education” and does not appear to count for high school graduation.

    • There is a “Robotics and Automated Technology” course that does count as a science credit for high school graduation.
    • UNB does not offer a BEd in CS, though they do offer a BEd in “technology studies”.* There is no CSTA chapter. Couldn’t find any stats.
      Nova Scotia:

    • High school students need to take “2 other credits from Technology, Mathematics or Science” for their diploma. There is no official computer science curriculum; the closest is a Computer Programming 12.

    • There are no BEd programmes in CS. There is no CSTA chapter. Couldn’t find any stats.
      Newfoundland and Labrador:

    • There’s no high school computer science curriculum at all (unless you count a course in HTML). There is a petition to change this.

  • I drank the critical theory koolaid.


    For our last LHA1803Y class of the term, we had a potluck in the class. I had fun putting together drinks:

    Positivist koolaid: your standard red koolaid. People will disagree what the red is supposed to be – cherry? strawberry? “red”? You grew up with it and remember it nostalgically but don’t really want to have some now that you’ve had more grown up drinks.

    Postpositivist koolaid: it’s the same red koolaid as the positivist koolaid, but your drink comes garnished with a lemon slice.

    Critical theory koolaid: the praxis of the sweet-yet-sour lemonade of theory with the harsh reality of ginger ale. While some would point out this is not actually “koolaid”, they’re missing the point. Drinking it makes you feel a little better about yourself.

    _Postmodernist “koolaid”: _there is no such thing as “koolaid”, nor the little umbrella that your drink is garnished with. After all, “koolaid” exists only through discourse and is socially constructed. A Foucaultian archaeological analysis indicates that the discourse originates through a combination of cranberry juice and tonic water, its bitterness a nod to Nietzsche – and the fact that the drink is an acquired taste that is generally seen as unpalatable without gin.

  • A quick and dirty introduction to Bourdieu for systems thinkers


    I’ve been on a Bourdieu kick for the course I’m currently taking on social theory (LHA 1803Y: Theory in Higher Education), and since Steve Easterbrook mentioned he wasn’t familiar with Bourdieu, I figured I’d write a quick and dirty introduction to Bourdieu’s social theories. Steve’s a systems thinker so this is written for such an audience.

    In systems thinking we like to think of people as existing in many (overlapping) social systems (because, after all, pretty much everything to a systems thinker is a system.) These social systems can be things like school, work, a professional community, or even your favourite internet community.

    Bourdieu would call those systems fields. (Specifically, a field is a system of social positions, with internal structure.) In his terminology, the rules determining the system/fields are known as nomos. (Fields are not the same as class, which I’ll get to later.) When people in fields ‘play by the rules’ of the system, and invest in it, he calls this illusio.

    If you’re wondering if he also has paradigms in his systems, the answer is yes! He calls them doxa, the concepts and ideas which go without saying as it comes without saying – “the universe of possible discourse”.


    As an individual, I interact with numerous fields. There are two things that matter about me in how I interact with these fields: my capital, and my habitus.

    Bourdieu distinguishes numerous forms of capital:

    • economic capital, how many financial assets I have
    • social capital, who I know, my social networks, what I can get out them, etc.
    • cultural capital, the knowledge, skills, advantages and education that I have – along with the cultural ‘know-how’ of how to navigate particular social situations
    • symbolic capital, the resources available to me on the basis of honour, prestige, recognition, or my other forms of capitalA thing worth noting about capital is that its value is context-dependent. For example, my knowledge of Star Trek trivia (which is cultural capital), has use in nerdy fields like computer science, but less so at gatherings of my extended family. Similarly, my Canadian money is of lesser value outside of Canada, and even less value were I to visit a society with a bartering or gift-giving economy.

    As for habitus, the description on Wikipedia wraps it up nicely: “the habitus could be understood as a structure of the mind characterized by a set of acquired schemata, sensibilities, dispositions and taste”. Your habitus is developed in part through socialization. Bourdieu conceived of habitus as a way to study the interaction area between individual and society – in software engineering terms you might think of your habitus as being the coupler between an individual and society.

    Every individual has a habitus – but likeminded individuals together can have group habitus. For example, a class habitus would refer to sensibilities, dispositions, tastes and ways of thinking about the world that are common to a social class.


    Unlike previous social theorists like Weber or Marx, Bourdieu’s view of class is not based on just economic capital, but instead on all capital (social, cultural, economic, symbolic). For me this is an appeal of Bourdieu: it explains why a ‘blue collar’ tradesman making the same amount of money as a ‘white collar’ adjunct are not really the same class.

    Much of Bourdieu’s work looks at the reproduction of social inequality. He identifies feedback loops (though not by name) of what keeps the classes separate – and cultural capital plays a major role. How much cultural capital a person has, and to what social standing they are born, are determine their social mobility.

    Those with a large amount of cultural capital in society are able to determine taste in society – such as what is low-brow vs. high brow (and everything in between). And people judge other people based on taste – does somebody ‘fit’ into a particular field? When people don’t fit in to a particular field, symbolic violence is used to keep them out or make them feel uncomfortable. Symbolic violence (also known as symbolic power) comprises things like implicit biases, microaggressions, de facto discrimination, and all that other lovely stuff that’s used to “keep them in their place”.

    Symbolic violence allows for the reproduction of social divisions. A kid growing up in a working class family is going to have less access to means of accumulating cultural capital, is more likely to be affected by class dispositions to not value education as highly, and is more likely to say and do things when interacting with intellectuals that make them stick out like a sore thumb. Indeed, Bourdieu focuses extensively on the little things that stop people (or allow people) to accumulate capital – what they wear, the things they say, the hobbies they have – for him it’s all about the gain on the feedback loops here. The word ‘accumulation’ tends to come up a lot.


    The stories women have of not feeling like they belong in tech/CS are the type of thing very amenable to a Bourdieusian analysis. Indeed, his early work focused on how the French university system amplified social inequalities (both in terms of gender and of class – and the intersection of the two, well before the term _intersectionality _took off).

    His work has been used quite a bit by sociologists to look at the reproduction of social inequalities – for example, a recent study used Bourdieu to examine how low-income minority ethnic groups feel alienated when they go to science museums – and why they don’t really go to them in the first place (hint: habitus plays a role – they have not been socially conditioned to see museums as a thing worth going to).

    In many ways Bourdieu was a systems thinker – he thought of feedback loops, systems-within-systems, and systems-level behaviours, structures, rules and paradigms – but did not have the systems thinking vocabulary available to him. While he was active at the same time as the Systems Thinkers in the English-speaking world, few Systems Thinking based books have been translated into French – and most of the translations happened after Bourdieu died in 2002 (for example, Limits to Growth wasn’t translated into French until 2012!)


  • Categorizing Interventions: Adapting the USI Model to CS Education


    I’m interested in studying diversity initiatives in CS education – and in doing so I consider it helpful to have a model of the different types of diversity initiatives that are used to recruit/retain women and other underrepresented groups in CS. But how can we come up with a useful model? This blog post is what I’ve come up with so far – where I started (explicit and implicit interventions) and where I recently arrived to (adapting the USI model of public health interventions to this context). It’s a work in progress and I’d love feedback.

    Explicit and Implicit Interventions

    First, I want to walk you through how I have mostly been thinking about diversity initiatives. I currently categorize them like so:

    • Explicit interventions: these target women (or other groups) and are explicit in their purpose. For example:
      *   Departmental women-in-CS clubs at many universities
      • The Grace Hopper Celebration of Women in Computing and similar conferences
      • Mentorship programmes for women in CS, like CRA-W’s
      • Outreach initiatives like Gr8Girls and Girlsmarts
      • Grassroots bootcamps/workshops like Black Girls Code and Ladies Learning Code
      • Awards/scholarships/grants for women, like the Anita Borg ScholarshipAll of these both are intended for women/girls, and in the process, the women/girls participating know the intervention is for women/girls.
    • Implicit interventions: these are stealthy – they are open to everybody and do not advertise the goal of supporting women in CS. Instead these are approaches which are known to benefit women disproportionately (and may also benefit dominant groups). For example:
      *   A CS professor uses [pair-programming](http://dl.acm.org/citation.cfm?id=1060075) and [peer instruction](http://scitation.aip.org/content/aapt/journal/ajp/74/2/10.1119/1.2162549) in their class, and [randomly calls on students in a structured fashion](http://dl.acm.org/citation.cfm?doid=1060071.1060073) -- all are known to disproportionately benefit female students -- but the professor does not tell her students she is doing this for the female students' sake.
      • A CS professor has their students write a value-affirming essay as an assignment at the beginning of term – this is known to help women overcome stereotype treat in male-dominated disciplines.
      • A CS department provides a mentorship programme to all students. A university mandates that all students need to take CS, and its CS department provides multiple, engaging, versions of CS1 that are tailored to different students’ interests, à la Harvey Mudd. A conference switches to using blind review of its submissions, which is known to disproportionately benefit women.
        The implicit interventions have a fairly different feel to them. For one thing, they tend not to just help women – these can also disproportionately help students of colour, students of low SES backgrounds, LGBTQ+ students, etc. These interventions change the system, rather than give underrepresented groups like women a buffer in an unwelcoming system.

        The implicit interventions also move away from singling out minority groups as though it is us women who have the problem, to instead working from an assumption that it is the CS classroom/department/workplace/etc that has the problem. (And btw, explicit interventions can cause stereotype threat: “we’re gonna help you because you’re a woman” is still reducing somebody to their gender.)

    Now, there’s some limitations to this model of explicit vs. implicit interventions. What if a professor did pair-programming in class but said it was to benefit the women? I’m not sure what I’d do with that. My categories were also clearly thought up about group-level interventions. In one of my committee meetings, Mark Guzdial asked me how one would categorize a CS professor spending extra time with a female student, encouraging her one-on-one. This also doesn’t really nicely fit into the current model.

    The Universal-Selected-Indicated (USI) Model of Suicide Prevention

    Greg Wilson recently retweeted a fascinating report on suicide in Toronto, which I was looking through earlier today out of curiosity. This particular section caught my attention:

    _4.2 Preventive interventions

    A public health approach to suicide prevention includes both universal interventions in the whole population and interventions targeted to key risk groups. Rose’s Theorem makes the case for prioritizing universal interventions because a large number of people at small risk may give rise to more cases of disease than a small number who are at high risk.[106,107]

    A model for understanding prevention interventions is the Universal, Selective and Indicated (USI) model, which breaks the targeted interventions down into selective and indicated interventions. [105] The USI model is a comprehensive way of categorizing prevention efforts according to defined populations, and consists of:

    • Universal interventions designed to reach the whole population, without regard to population target groups or risk factors;
    • Selective interventions are designed to focus on groups who have been identified as at high risk for suicide-related behaviours; and
    • Indicated interventions are designed for individuals showing signs of suicide-related _
      This immediately made me think of my explicit/implicit intervention model. My ‘implicit interventions’ are universal interventions (though stealthy), ‘explicit interventions’ are selective interventions, and the one-on-one interventions that Mark asked me about would be indicated interventions. The conversion isn’t perfect: public health scientists think about ‘the population’ whereas in education we could be thinking about a classroom, a department, a programme, all the kids in grade 8 across a country, etc – our ‘population’ is flexible.

    The fascinating thing for me is the analysis of universal interventions – which the report lauds as being be more effective, and cites meta-analyses finding universal interventions being generally be more effective than selective interventions.

    Digging through the public health literature on USI, this certainly seems to be a trend. Selective interventions can do good work, but universal interventions seem to go that extra mile.

    A USI Model for Diversity Initiatives in Education

    After spending my afternoon reading public health papers using the USI model, I’d say the way I now think about diversity initiatives takes this form:

    • Universal interventions are intended to affect a whole body of students (or professionals, etc), such as a whole classroom or a whole degree programme. Universal interventions make change in an educational system in a way that disproportionately benefits underrepresented groups, but also has a positive or neutral effect on dominant groups. Examples include:

    • A whole CS classroom using pair-programming

    • Blind review for conferences
    • Everybody has to take some CS* Selective interventions target a population known to be underrepresented in computer science (e.g. women, people of colour, low-SES, etc), are offered specifically and explicitly to that group, and provides them with targeted support to ‘level the playing field’ with dominant groups in CS. Examples include:

    • Women in CS conferences/celebrations

    • Outreach events for girls* Indicated interventions are individual-level interventions – such as a teacher or professor taking the time to give extra encouragement to a student to study (or stay in) CS.
      All three types of interventions can have positive impacts on diversity in CS. One-on-one encouragement is, for example, a strong indicator of whether black students will take CS. And supportive communities like Grace Hopper can help women find a place in the CS community.

    But like in public health, universal interventions can make the widest changes on a population-level at a lesser cost. Not every student interested in CS can (or will be) reached by selective/indicated interventions. It would be infeasible to get CS outreach efforts to reach every single girl, kid of colour, kid of low SES, etc – “[w]e need [to get CS into] school in order to reach everyone.