What CS Departments Do Matters: Diversity and Enrolment Booms

I've written before about the historical factors that have led to the decline in the percentage of women in CS. The two enrolment booms of the past (in the late-80s and the dot-com era) both had large impacts on decreasing diversity in CS. During enrolment booms, CS departments favoured gatekeeping policies which cut off many "non-traditional" students; these policies also fostered a toxic, competitive learning environment for minority students.

We're in an enrolment boom right now so I --- along with many others --- have been concerned that this enrolment boom will have a similarly negative effect on diversity.

Last year I surveyed 78 CS profs and admins about what their departments were doing about the enrolment boom. We found that it was rare for CS departments to be considering diversity in the process of making policies to manage the enrolment boom.

Furthermore, in a phenomenographic analysis of the open-ended responses, I found that increased class sizes led many professors to feel their teaching is less effective and is harming student culture (this hasn't been published yet --- but hopefully soon!)

Around the same time I put out my survey, CRA put out a survey of their own on the enrolment boom. Their report has just come out; they have also found that few CS departments are considering diversity in their policy making --- and that the departments who have been considering diversity have better student diversity.

From CRA's report:

The Relationships Between Unit Actions and Diversity Growth

The CRA Enrollment Survey included several questions about the actions that units were taking in response to the surge. In this section, we highlight a few statistically significant correlations that relate growth in female and URM students to unit responses (actually, a composite of several different responses).

1.    Units that explicitly chose actions to assist with diversity goals have a higher percentage of female and URM students. We observed significant positive correlations between units that chose actions to assist with diversity goals and the percentage of female majors in the unit for doctoral-granting units (per Taulbee 2015, r=.19, n=113, p<.05), and with the percent of women in the intro majors course at non-doctoral granting units (r=.43, n=22, p<.05). A similar correlation was found for URM students. Non-MSI doctoral-granting units showed a statistically significant correlation between units that chose actions to assist with diversity goals and the increase in the percentage of URM students from 2010 to 2015 in the intro for majors course (r=.47, n=36, p<.001) and mid-level course (r=.37, n=38, p<.05). Of course, units choosing actions to assist with diversity goals are probably making many other decisions with diversity goals in mind. Improved diversity does not come from a single action but from a series of them

2.    Units with an increase in minors have an increase in the percentage of female students in mid- and upper-level courses. We observed a positive correlation between female percentages in the mid- and upper-level course data and doctoral-granting units that have seen an increase in minors (mid-level course r=.35, n=51, p<.01; upper-level course r=.30, n=52, p<.05). We saw no statistically significant correlation with the increased number of minors in the URM student enrollment data. The CRA Enrollment Survey did not collect diversity information about minors. Thus, it is not possible to look more deeply into this finding from the collected data. Perhaps more women are minoring in computer science, which would then positively impact the percentage of women in mid- and upper-level courses. However, units that reported an increase in minors also have a higher percentage of women majors per Taulbee enrollment data (r=.31. n=95, p<.01). Thus, we can’t be sure of the relative contribution of women minors and majors to an increased percentage of women overall in the mid- and upper-level courses. In short, more research is needed to understand this finding.

3.    Very few units specifically chose or rejected actions due to diversity. While many units (46.5%) stated they consider diversity impacts when choosing actions, very few (14.9%) chose actions to reduce impact on diversity and even fewer (11.4%) decided against possible actions out of concern for diversity. In addition, only one-third of units believe their existing diversity initiatives will compensate for any concerns with increasing enrollments, and only one-fifth of units are monitoring for diversity effects at transition points.

From a researcher's perspective this has me happy to see: we used very different sampling approaches (they surveyed administrators, I surveyed professors in CS ed online communities), we used different analytical approaches (their quantitative vs. my qualitative), and we came to the same conclusion: CS departments aren't considering diversity. This sort of triangulation doesn't happen every day in the CS ed world.

CRA's report gives us further evidence that CS departments should be considering diversity in how they decide to handle enrolment booms (and admissions/undergrad policies in general). If diversity isn't on policymakers' radars, it won't be factored into the decisions they make.

"Helping" women in CS with impostor syndrome is missing the forest for the trees

Alexis Hancock recently wrote an article on impostor syndrome that has been on my mind ever since, as it adds so nicely to a blog post I wrote several months ago. I wanted to try and explain why so many women have impostor syndrome in CS:

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).  [...]

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. [...]

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.

When [people] go on to play roles [they haven't been raised for], 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.

I'm far from the first person to argue that impostor syndrome comes from environmental cues. What Hancock's article does is point out the contradiction: impostor syndrome has environmental causes, but is talked about as being an individual's personal problem.

[While struggling with impostor syndrome] I became consumed with proving myself. Still, all the advice I received came in the form of a pep talk to “believe in myself” again. This common response to the struggles of women in tech reinforces the idea that imposter syndrome is the ONLY lens to view and cope… but the truth is, our negative experiences in tech are usually outside of our control. The overwhelming focus on imposter syndrome doesn’t provide a space to process the power dynamics affecting you; you get gaslighted into thinking it’s you causing all the problems.

Similarly, Cate Hudson writes that:

Yet imposter syndrome is treated as a personal problem to be overcome, a distortion in processing rather than a realistic reflection of the hostility, discrimination, and stereotyping that pervades tech culture. [...] Assuming that it’s just irrational self-doubt denies potentially useful support or training. Most of all, chalking up myriad factors to such an umbrella term belies the need to explore where these concerns arise from and how they can be addressed or mitigated. Subtle or not-so-subtle undermining behavior by colleagues? Gendered feedback? Lack of support or mentorship? [...] We pretend imposter syndrome is some kind of personal failing of marginalized groups, rather than an inevitability and a reflection of a broken and discriminatory tech culture.

So many well-intentioned diversity efforts in computer science focus on impostor syndrome and try to help women cope with it. But that discourse treats the women who have impostor syndrome as though they have an individual problem. The effect can silence women: instead of seeing their negative environment as a structural issue, they blame themselves.

Those of us who want to get more women into CS need to stop telling women that they suffer from impostor syndrome and instead help them see environment they're in. The social cues that are affecting them need to be identified and mitigated. And we need to stop teaching women to blame themselves for the sexism around them.

"'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" 

Generational differences of female scientists in academia

In my last post, I described how the experiences of women in CS have changed historically. In this post, we saw that the academic side of computer science is a relatively recent thing. For this post, I'd like to focus some more on that aspect of the history. Like that last post, this post will be specifically focusing on North American CS (we've seen previously that female participation in CS is different outside the West!).

Generational differences exist between female scientists in academia. Etzkowitz et al in a 1994 paper found differences in experiences and values between the trailblazing "First Generation" of women in a field, and the subsequent "Second Generation". As the paper is now 20 years old, it's not too surprising that it feels a bit out of date -- what comes after the Second Generation? (Another dated thing about the paper is that CS is described as being as female-friendly as biology.)

The Etzkowitz et al paper studied 30 academic science departments (biology, chemistry, physics, CS, and electrical engineering). They went into the study interested in the notion of critical mass -- whether having enough women in a department would lead to a positive feedback cycle leading to gender equality. (Answer: it's not that simple.) In the process of studying critical mass, they found the women who had entered the field before it was attained (First Gen) had fundamentally different experiences than the women who entered after.

Women in CS: A Historical Perspective

Female participation in computer science in North America has varied a great deal over time. Women were the original "computers" before the days of computing machines -- and then were hired as the low-status "coders" to run those machines. Over time, coding/programming was more widely recognized to be difficult -- and it was shifted from being "women's work" to "men's work".

When computer science emerged as an academic discipline in the 70s and 80s, women were well-represented (30-40%). As enrollments in CS programmes exceeded what departments could manage, they tightly restricted the paths one could take into a CS major -- unintentionally pushing non-traditional students like women out of the field. A big lesson from that period is that non-traditional students come from non-traditional paths -- many of these women were starting in majors such as psychology or linguistics, or transferring from community colleges, and hence did not follow the "standard" path into computing careers.

Why are there more women in some STEM fields than in others?

Why is it that there are more women in biology than there are in computer science in North America? Women in the biomedical fields are now earning more than 50% of undergraduate degrees in the US [1].

Biology, like computer science, was once stereotyped as masculine. Medicine continues to be stereotyped as masculine, especially fields such as surgery. Why has biology attracted so many more women than computer science?

To answer this question, I'll be synthesizing the findings of Cheryan's "Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?" [2], Cohoon's "Women in CS and Biology" [3], and Carter's "Why students with an apparent aptitude for computer science don’t choose to major in computer science" [4].

Between these three papers, four themes emerge for why women choose one STEM field over another:

1. Exposure to the field
2. Expected value of the major
3. Lack of prejudice in the scientific culture
4. Prospects of raising a family in that scientific culture

Why Are There More Women in CS in Other Cultures?

The rates of female participation in CS -- and STEM in general -- vary wildly from culture to culture. In the US, women currently make up about 18% of undergraduate CS students [1], but over in Qatar, women make up about 70% of CS undergrads [2].

Women in STEM are better represented in countries such as Turkey, Hungary, Portugal, and the Philippines. In these countries, women make up approximately 50% of STEM undergrads [3]. Indeed, well-developed countries like Canada, the US, and the UK have some of the lowest levels of female participation in STEM.

So, what cultural factors lead to fewer or more women in STEM? Per the work of Barinaga, there are five factors [3]:

1. Recently developed science capabilities, resulting in an unentrenched scientific community
2. Perception of science as a low status career
3. Class issues that overshadow gender issues
4. Compulsory math and science education in secondary school
5. Large social support for raising families

Computer Science as a Lake

Imagine your CS department is a lake.

The fauna of your lake are primarily fish and frogs. Normal lakes in your biome tend to have a food chain where about half of the predators are frogs and the other half are fish.

Back in the 80s, the predators in your lake's ecosystem used to be 40% frogs. But then the frogs started dying, or leaving, or not hatching, or whatever else caused their population to plummet. Now you're at 25% frogs. And even though it looks like the fish are doing fantastically, there's less of them than there should be.