• A Reading List for Critical CS Education

    One of the challenges of studying equity in computing is that while there is a lot of work on the subject, the topic lacks the infrastructure of a well-established (sub)field. For example, when I was a PhD student studying for my qualifying exam, there was not a pre-established list of texts for me to study. When students and colleagues ask me for readings on equity in CS I tend to start from scratch each time, and will miss things.

    To solve these issues, I’ve put together a reading list on critical prespectives on equity in computing. The list is heavily annotated for guidance, and importanty covers foundational texts in areas that a criticial approach to computing equity should draw on (e.g. critical pedagogy, Science and Technology Studies, Gender Work & Organization, critical disability studies, critical race studies).

    I welcome feedback on the list. Hopefully you’ll find it useful!

  • Spoon Theory: A Form Of Capital

    As social stratification is something that sociologists study, it’s also something that we sociologists have spent a fair bit of time thinking and theorizing about. One of our modern understandings of class & social stratification comes from French sociologist Pierre Bourdieu. Bourdieu argued that there are multiple forms of capital which together determine class.

    In this essay I argue that spoon theory, a common metaphor for units of physical/emotional energy used in disability circles, are a Bourdieusian form of capital. I’ll explain Bourdieu’s forms of capital, spoon theory, and why “spoons” as a unit of energy are a form a capital. Thinking of spoons in this framework is something that would be useful in social theory, as well as disability studies.

    Forms of Capital

    Bourdieu listed three forms of capital in his work:

    1. Economic capital: how much money you have, assets, etc
    2. Social capital: who you know
    3. Cultural capital: your knowledge, intellectual skills, and ability to navigate particular social situations. A subtype of cultural capital that is often discussed is linguistic capital — the language you have at your disposal.Thinking of multiple forms of capital allows us to have a more nuanced notion of class. For example, a graduate student with very little economic capital often will have a great deal of cultural capital.

    ####

    Capital Begets Capital

    Each form of capital can be used to get more of itself (a positive feedback loop):

    1. If you have more economic capital, you can invest the money to get more money. If you have more money, you can buy things in bulk, or buy higher quality items that do not need to be replaced/maintained as often.
    2. If you have more social capital, you can similarly use it to get more social capital. If I know many people, I can ask my many connections to get me in touch with more people.
    3. If you have more cultural capital, you can use it to get more cultural capital. If I have a university degree I am more familiar with how universities function and am better able to navigate the process to get additional degrees or apply to academic jobs.And with all of these forms of capital there is the possibility of a poverty trap. For example: if you don’t know anybody, it’s harder to meet more people. Capital also has inter-generational effects. A parent with more capital can pass their capital onto their children.

    Those who have a given form of capital often take their capital for granted. A rich person does not worry or think very much about spending money. A highly educated person can take it for granted how difficulty it is to get into (and stay in) university, since they likely did not struggle with a lack of cultural capital during their education.

    Capital is Convertible

    These different forms of capital are not separate. For example:

    • If you have more economic capital, you can attend events where you meet people and increase your social capital. You can also attend more “elite” universities wherein you can increase your cultural capital.
    • If you have more social capital, you can find more business partners and talk to people with sound financial advice. Similarly, you can find people to help you get into university, or into a prestigious job.
    • If you have more cultural capital, you will have an easier time getting a bank loan. Also, you can leverage your alma mater’s alumni association to meet people and get social capital.Thinking about capital beyond economic capital is often a useful lens for sociologists when it comes to understanding social stratification. Some of what Bourdieu is known for is his analyses of who in society gets a higher education, which depends on all three of his forms of capital.

    What are Spoons / Spoon Theory?

    In 2003, Christine Miserandino found herself trying to explain to a friend what was like to live with lupus. They were at a diner. Looking for a prop to demonstrate her limited energy, she grabbed spoons from nearby tables:

    _”Most people start the day with unlimited amount of possibilities, and energy to do whatever they desire, especially young people. For the most part, they do not need to worry about the effects of their actions. So for my explanation, I used spoons to convey this point. I wanted something for her to actually hold, for me to then take away, since most people who get sick feel a “loss” of a life they once knew. If I was in control of taking away the spoons, then she would know what it feels like to have someone or something else, in this case Lupus, being in control.

    I asked her to count her spoons. She asked why, and I explained that when you are healthy you expect to have a never-ending supply of “spoons”. But when you have to now plan your day, you need to know exactly how many “spoons” you are starting with. It doesn’t guarantee that you might not lose some along the way, but at least it helps to know where you are starting. She counted out 12 spoons. […]_Miserandino then proceeded to illustrate to her friend how daily tasks such as getting out of bed, getting dressed, showering, washing her hair, each cost spoons. If she ran out of spoons, she had to stop and rest to recover spoons. For a video description using the Sims, check out Jessica Kellgren-Forzad’s description of spoon theory.

    The metaphor caught on in the disability community as an emic descriptor, particularly online. The term “spoonie“ refers to people who live with limited spoons, such as due to autoimmune disorders, neurological disorders, connective tissue disorders, sleep disorders, chronic pain, chronic illness, and other disabilities where energy is scarce. These types of conditions are often co-morbid with each other, and it gives an unifying label for people who have multiple energy-limiting conditions.

    It’s common for spoonies to describe their activities and lived experiences in terms of spoons. The term spoon has a well established meaning in the disability community as a unit of exertion. I’ll be using the term “spoon” in this way throughout the rest of this article, to reflect that I am using a concept that has been both labelled and accepted by the disability community.

    Let’s Get Autoethnographic

    To get personal: I identify as a spoonie, and the day I learnt about spoon theory was an emotional day for me. It gave me a label to describe my experience, and terminology to explain my life. An incomplete list of my medical history includes myofascial pain, fibromyalgia, IBS, depression, migraines, anaemia, night eating syndrome, and a yet-unspecified hypermobility spectrum disorder.

    Each one of these conditions can severely limit the amount of spoons I have in a given day. I wake up each day not knowing how much energy I’ll have or how much pain I’ll be in today. I have to carefully ration my spoons and spend a great deal of mental energy budgeting my spoons.

    My medical history contains many co-morbid diagnoses of exclusion, and a list that keeps evolving since many of these conditions are only recently being researched by modern medicine. Having the term “spoonie” available to me gives me not only a consistent label, but one that acts as shorthand instead of having to list all my syndromes/conditions.

    It feels very personal for me to share my medical history. Using the term spoonie allows me to communicate my personal situation without having to disclose my medical history. It connects me to a community of other people who have to think about every spoon they spend every day — a currency not worried about by most people.
     

    Using the Term “Spoons” 

    When I showed friends and colleagues earlier drafts of this article, a common response was that my use of “spoons” felt unusual to them. I spent some time wondering if I should use a more academic-sounding term, such as energy, or exertion. But I see two main reasons why I should keep using the term spoons here:

    1. So the article is accessible to people in the disability community. A common problem with academic articles is we use use different terminology, and so the people who would benefit from reading the article often do not have the linguistic capital to find it.
    2. This is the language that is used in the disability community. It may sound strange if it’s new to you. That’s normal when you see a new term, particularly one outside your habitus. Consider the rest of this article a chance to get familiar with this new term.This isn’t the first academic text out there to use the spoon metaphor: for example there is this clinical study of patients with MS, and this paper about pain communication on Tumblr.

    Spoons are a Resource

    Spoons are a representation of units of physical, emotional, or cognitive energy. Another metaphor often used for this is a battery: energy is spent and can be recharged.

    Spending a spoon refers to an activity which requires significant exertion. Spoons can be recovered through resting.

    What costs spoons does vary from person to person. It costs me a spoon to get out of bed in the morning due to the physical exertion. But if getting out of the bed in the morning is something you can do without thinking about, likely it isn’t costing you a spoon.

    Similarly, while it costs me numerous spoons to get to work, once at work I generally expend few spoons. It doesn’t cost me a spoon to sit and participate in a discussion on advanced statistics, or to read a paper on social theory, or to meet with a colleague. But for somebody with a learning disability all of these things likely would cost them spoons.

    Whether an activity expends spoons is not the same as difficulty. It may be difficult for me to wrap my head around a particularly esoteric scholarly work, but the process does not feel like it drains my energy. Spoons are also not the same as disability. Being deaf or blind does not mean you have a stock of sight or hearing to spend each day. Furthemore, abled people expend spoons; the issue is that they do generally not worry about them.

    If you look at Jessica Kellgren-Forzad’s Sims example, she compares a day in the life of “Alice” who has a chronic illness, to “Mary” who does not. Alice begins her day with 10 spoons and keeps having to stop throughout her day to rest as she is frequently on the brink. Mary, in contrast, begins her day with 30 spoons, and ends it with 20, not having to worry about her energy as she goes through her day.

    Spoons are a Form of Capital

    People who have abundant spoon capital_ _available to them do not worry, or often think, about how  much they have. It is not a limited resource to them. Indeed, it probably wasn’t a limited resource for Bourdieu — and so not something he would have thought of in making his social theories.

    Like other forms of capital, spoon capital begets more spoon capital:

    • Let’s say I start my day with five spoons. It takes me one spoon to get out of bed. One spoon to get dressed. One spoon to brush my hair. One spoon to brush my teeth. I’m now down to one spoon and need to spend my last spoon getting back into bed to rest. Resting gives me back three spoons. It took five spoons for me to get ready for work.
    • The next day I wake up with six spoons. It then takes me four spoons to get ready for work because I don’t have to spend spoons to rest in the process of getting ready. Having more spoons to begin with means the chance to more efficiently expend spoons.I mentioned I spend time budgeting my spoon capital. When I was an undergraduate I spent time budgeting and fretting over literally every cent I had. Budgeting my spoons feels the same way to me._ _Like those who need to track every cent they have in order to keep afloat, I have to pay attention to each one of my precious spoons just to function in our society.

    Spoons are a discrete resource. They can be quantified. They can be tracked. Indeed, I track mine: I keep a spreadsheet diary of my daily spoons and use it to plan and keep track of what I do. (Yes, I am a nerd and I love spreadsheets.) I have a pretty consistent idea of what one activity costs in terms of spoons, and so can reliably measure them.

    Spoon capital is not the same as health. Unlike in an RPG where somebody has health or hit points, it’s quite difficult to come up with a quantifiable, measurable way of saying how much health somebody has. Health is a categorical form of data, not a numerical one. Health is also not spent or recharged in the same way that spoons are.

    Depending on who you are, activities could cost different amounts of spoons for you. This is true of other forms of capital. For example, I don’t have to spend economic capital to read a paywalled scholarly article because I have the cultural capital of institutional access. Likewise, somebody who has the economic capital to get a fancy credit card that gives them free lounge access at an airport does not have to pay the fee to enter the lounge.

    Spoon Capital is Convertible to Other Forms of Capital

    Like how Bourdieu’s three forms of capital are affected by each other, spoon capital is related to the other forms of capital. For example:

    1. When I have more spoon capital at my disposal, I can do more to price shop. I can go to more stores, and compare more prices, or go to a store farther away to get a better deal. It also costs me spoons to buy in bulk. It takes me more spoons to carry a larger/heavier load home from shopping. 
    2. More spoon capital also means more social capital. With more spoons I can do go to more parties and events, or last longer at the same events — meaning I can interact with a larger group of people.
    3. And spoon capital can unlock cultural capital. You need energy to be a student and to finish a university degree. Students with disabilities often spend many of their precious spoons on getting documentation for their disability, navigating university bureaucracy to get accommodations.And these can all go the other way:

    4. With more economic capital, you can buy more mobility aids which will help you preserve your spoons and spend them more efficiently. I bought a recumbent bike ($$) since I can’t ride an upright bike, and riding it costs me fewer spoons than having to deal with public transit. (Driving is an issue for me.)

    5. More social capital means you’re more likely to know people with similar conditions who have helpful management strategies, helpful doctors, good physiotherapists, etc.3. And cultural capital means a that you can stay on top of new clinical research, have more productive discussions with health professionals, and unlock medications/treatments that could give you more (or fewer!) spoons.Spoon capital does not neatly fall into Bourdieu’s framework of three fundamental forms of capital. Spoon capital may be converted into or from other forms of capital but is still a distinct stock of capital. It is embodied, and often quite fixed — I can use my economic, social and cultural capital to find ways to spend my spoons more efficiently, but it is rare to find a way to actually increase the total number of spoons I have at hand.

    Conclusions

    Spoon capital is a distinct form of capital that is missing from Bourdieu’s 1985 framework of forms of capital. Like other forms of capital, it can be overlooked and taken for granted by those who have the given form of capital. And like other forms of capital, those without can find themselves in a poverty trap, particularly if they do not have other forms of capital to leverage. Using a disability lens gives sociologists a way to identify aspects of social life which would otherwise be missed by traditional sociology.

    For those who have little spoon capital, thinking about spoons as capital not only unlocks useful language to communicate about our spoon-poverty, but also surrounding the relationships between being a spoon capital and economic, social and cultural capital. This lens can be used to give insight surrounding disability and social stratification.

  • What's different between female STEM workers and those in other professions?

    Many studies of women in STEM use men as a referent group to women: how do women compare to men in CS with regard to retention, attitudes, discrimination, etc? While there’s certainly benefit to using men as a referent group (and it’s far, far better than no referent group at all), there’s a threat to validity that we tend to overlook when studying women in CS: how much of what we see is an artifact of CS culture versus that of our wider society? **
    **
    Triangulation using different referent groups is a good way to get around this issue. I’ve talked before about differences between women in CS vs. other STEM fields, differences between women in CS between different cultures, and differences over time/generations. But in every one of these posts, I’ve really only looked at scientists.

    Glass et al’s “What’s So Special about STEM? A Comparison of Women’s Retention in STEM and Professional Occupations“ addresses another angle: what’s different for women in STEM vs. women in other professional occupations? After all, women are more likely than men to leave other professional occupations such as business, medicine and law [1]. And in all these fields, substantial problems remain at the top: women may make up a substantial proportion of workers, but a tiny minority of those running the show.

    The Glass et al Paper

    To make the comparison of STEM women and non-STEM women, the Glass et al paper uses longitudinal data from the National Longitudinal Survey of Youth 1979. The longitudinal approach is a strength of the paper. A weakness, however, is that the women participating are a single generational cohort who entered the workforce in the late 80s/90s: “second generation” per my previous post.

    Overall, Glass et al found that women in STEM jobs had more in common with women in non-STEM professional jobs – and that “few differences in job characteristics emerge” overall. This is a rather important finding – it means that if we work carefully, we can often generalize findings about women in the general workforce to women in the STEM workforce.

    I say “carefully” since there were a few differences that they found. Here’s what’s unique to STEM women:

    1. Women who are married to fellow STEM workers are nearly 100% more likely to stay in their STEM jobs than women married to non-STEM workers.2. A higher education does not increase a STEM woman’s likelihood of staying in a STEM career. In other occupations, such as medicine or law, the more advanced degrees a woman has, the more likely she’ll stay in the field. Glass et al attribute to this to the type of work done by those with MSc/PhDs: the more education you need to do the job, the more likely it’ll be isolating and in a “noxious” work environment for women.
    2. Unlike non-STEM women who leave their jobs to stay at home, when STEM women leave their jobs, they overwhelmingly do so to fill non-STEM jobs, rather than to stay at home permanently. Switching to management explains almost a quarter of these job departures.Those three differences aside, everything they looked at turned out to be the same for both STEM women and non-STEM women.

    Similarities between female STEM and non-STEM workers

    For both women in STEM jobs and women in professional non-STEM jobs, the following things are positively correlated with the retention of women in the workplace: Higher pay, job commitment, higher reported job satisfaction, longer time working in that career, and the presence of parental leave.

    Sociologists have documented the “Work-Family Narrative“ – the cultural narrative that women leave (or struggle with) their jobs because they can’t balance work and family. They’ve similarly documented that the majority of workplace interventions to improve the status of women focus on this narrative.

    Yet, what Glass et al found is that primary propellant of women out of the workforce – both STEM and non-STEM – is not childcare. Nor is it lack of confidence or lack of training – or lack of “leaning in”.

    The primary propellants are dissatisfaction with pay and promotion prospects. There’s a ton of sociology papers out there finding similar results. Childcare might be the catalyst for acting on that dissatisfaction, but it’s not the underlying cause.

    This dissatisfaction is linked to a number of sources of inequality, such as being left out of the “boys networks”, subconscious biases against women, open prejudice about the competence of women, and sexual harassment. Correspondence studies of women in STEM and other professional domains have consistently found that women are less likely to be thought worth of a promotion as an equally qualified man, less worthy of a higher salary, and less likable overall. And there’s evidence that men in our society are promoted based on potential – while women are promoted based on past accomplishments. This sort of unintentional, de facto discrimination is not unique to STEM.

    The Work-Family Narrative as a Social Defense

    The paper I linked to about the “Work-Family Narrative” – by Padravic and Ely – presents a rather compelling argument that the reason that people focus on this narrative is because it is an unconscious social defense. The Work-Family Narrative gives a way of thinking about the problems facing women in the professional workplace that doesn’t involve coming to terms with discrimination and systematic problems in the workforce.

    Padravic and Ely also argue that this narrative similarly allows people to keep their cultural stereotypes in tact: women are the caregivers, men are the workers – and so women have a hard time in the workforce because they must balance their position as caregiver. I’ve noted before that our brains are wired to keep cultural stereotypes in tact.

    Discrimination is an ugly thing to talk about. I don’t blame people for shying away from it. But it needs to be tackled to change the numbers of women in the workforce – whether it be STEM or other fields. And it’s important to compare STEM to the rest of society – we need to know what’s a STEM problem and what’s even more systematic.

  • 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.

  • Introducing Computer Science via Online Security: An Experience Report

    Last weekend I spent two hours teaching an informal introduction to online security to an audience of political activists. I wound up teaching a fair bit of computer science in the process and I’m writing up this experience report because I think it’s a valuable way to teach introductory computer science.

    Before I put together my lesson plan I spent a fair bit of time looking at other people’s introductions. Broadly, they fell into two categories:
    1. Introductions for CS students, which would include things like how to write your own HTTPS server or proofs about why RSA works (too advanced for my audience)
    2. Instructions for what software you should download to stay secure.

    I’m a member of the political organization from which my audience came. People regularly post articles which fall into category 2 on the online community for the group. And not unsurprisingly, these articles have had limited effects on getting people to change their behaviour. This was why I’d volunteered to teach the workshop. I’d initially planned it to be all about the software to install to stay safe.

    As I put together my lesson plan I had a change of idea for the goal of the workshop. In my experience teaching introductory programming, students struggle for the first few weeks because they don’t understand why they should be learning this or what it gets them. I started to think something similar might be going on here: a typical article telling you to install Signal and HTTPS Everywhere doesn’t sufficiently motivate why it’s necessary and what’s going on technically.

    Computer scientists like myself think of the internet in a very different way than my activist friends. My activist friends see the internet as a mystical black box.

    My learning goal for the workshop hence became: to demystify the internet.

    What I taught

    I gave some homework to my “class”: to watch this series of videos from code.org on how the internet works. I’d spent some time on youtube watching videos on how the internet works and conclude those were the best out there.

    The videos are quite lovely and well-produced. They, however, do something I don’t like: they talk about data as being mystical 1s and 0s. So I started the workshop with demystifying how data is stored.

    Files and Encodings

    I went over character encoding. We talked about ASCII, unicode, and encodings for languages other than English. I talked about how this entire setup was America-centric, and the pains that non-English writers have had as a result.

    From there we talked about other file encodings. I walked through an extremely simplified bmp encoding. We then talked about compression and encodings like jpg. I hadn’t expected to bring compression into the mix but it came up in the questions.

    I then asked the group, “so what is a file?” I got the same blank looks I get from my first year computer science students when I introduce file I/O. Most computer scientists tend not to realize how much difficulty novices have with the concept of a file.

    In our group discussion about files I wound up explaining what virtual memory is and some basics about file systems. This was another piece of computer science that came up through class discussion that I hadn’t expected would come up (but was excited to see!)

    We then talked about metadata and, from there, how much information you can get from somebody’s metadata.

    Networking 

    I then shifted gears to talk about “suppose we want to share a file”. From the videos my audience already had seen the notion of a packet. We talked about how a file (and any other information) will be broken into packets to be sent over a network.

    I then talked about pre-internet networks. I talked about hubs and routers and in retrospect I should have left out hubs. I think hubs added confusion.

    We then walked through an example of how UDP works.

    Internetworking

    Then I started talking about internetworking, and how the internet is a network of networks. I explained what a LAN is, then a WAN. Everybody had heard of an ISP before but was kind of fuzzy on what they do.

    The code.org videos didn’t go into what ISPs do or how data is shared between ISP. I talked about IXPs, the internet backbone, and the landing stations for intercontinental cable/fibre lines — and how those are common targets for government eavesdropping (see: Diebert’s “Black Code”).

    In retrospect I wish I’d spent even more time on that part, and talked about tiers of ISPs, as well as net neutrality. I wish I’d also shown a couple examples of traceroutes and how data sent from a computer in Toronto to a computer in Vancouver will most likely go through the USA, which undermines legislation trying to keep sensitive data on Canadian servers.

    Once we’d covered how the internet is structured, we talked about TCP then IP. We talked about IP. Again we returned to how the internet has been structured in an America-centric fashion: how IPv4 addresses were allocated.

    Were I to do this part again I’d spend more time on it and talk about RIRs and how they’re governed.

    From IP addresses we talked about DNS. Again, more American neocolonialism was discussed with how TLDs were setup. We talked ICANN. My audience was fascinated by learning about ICANN and similar governance bodies and we would up on a tangent about how FOSS works and how to get involved in FOSS projects.

    We then talked about HTTP, and the protocol stack. We talked about some other applications such as SMTP, IMAP, XMPP, etc.

    I talked about ports and sockets and regretted it because I don’t think I did a good job of it. I don’t think it added much to their understanding either.

    At this point we’d been going for a bit over an hour and I figured this was a good place to stop and see if they had any questions about how networks work. One participant made an observation that the internet doesn’t seem to have been designed to be secure (yep) and we talked about this in more detail. Another participant asked about VPNs so we talked about those, but probably not in a satisfactory level of detail. I mentioned TOR in this discussion but didn’t do a very good job of explaining how TOR works — were I to do this again I would spend more time there.

    Cryptography

    After all this network talk, I shifted gears to talk about cryptography. I went over symmetric key encryption. As I went through it I wish I’d actually done this before talking about encoding, because there was confusion about whether the text or the encoding is what encrypted/decrypted.

    I talked about how the key is often the weakest link in symmetric key encryption and then started talking about Whitfield-Diffie. I gave a high level overview of asymmetric key encryption. At this this point I was running kind of behind where I expected to be so I rushed this, which was a shame. There was a fair bit of confusion about public vs. private keys, which is fairly confusing for novices (especially if you aren’t shown the underlying mathematics.)

    I talked about why asymmetric key encryption was necessary for the internet to work as we know it. Had I more time I would have loved to get into talking about P ?= NP.

    Secure Networking

    We then got back to networking. I talked about SSL and HTTPS, and what it means when something is end-to-end-encrypted. I did not talk about certificate authorities due to time constraints but I wish I had.

    I then gave them this link to tools for security, and mentioned a few of my favourites. I explained that security is a process, not an end-result, and one of my participants asked, “so how do we keep up to date on what’s secure?” and I still wish I had a good response for him. Most ways I keep up to date on these things are written for a tech-savvy audience.

    Finally we talked about human factors in security. This xkcd came up. We talked about the DNC email leaks.

    We then wrapped up. People told me I’d done a lot to demystify the internet for them. Heartingly, a bunch of people at the seminar have since installed many of the tools I told them about.

    Discussion

    One thing I really liked about teaching this workshop was how much the students could talk about what’s going on. When I introduce CS via programming, it’s much harder to teach it in a student-directed fashion because the students have very little idea where to go next. With “how does the internet work?” my students had so many questions.

    I’d gone into the workshop with a lesson plan but then wound up covering things in different order because a somebody would ask a question and we’d go that direction. It was quite exciting for me to teach CS this way.

    Another nice thing about introducing CS via the internet vs. via programming is that this way we show the history of CS. CS is shown as a human endeavour that builds upon itself. You don’t really get to show this in the process of teaching programming to novices.

    How I’d Teach It Again

    If I were to do the workshop again, I’d take four hours (two felt too rushed), with some breaks in there. I’d order it as:

    • what is an algorithm?
    • symmetric key crypto
    • files and encoding
    • computer networking
    • internetworking
    • asymmetric key crypto
    • how to keep safe

    But better yet I’d love to teach this as a 12-week university course. There was so much in there that could be used to introduce computer science and garner interest from new communities. This course would complement any intro-to-programming class and they could be taken at the same time.

    I’ve written up what I’d cover in the 12 weeks here.

    I think a student taking such a course would walk out with a better sense of what CS is about than if they’d taken an introductory programming class. Certainly programming is a useful skill that many people benefit from learning (not just CS people), but many people walk out of their first CS class with the misconception that CS = programming.

    The material in this course is useful to a broad segment of society. Everybody uses the internet, but few people understand how it works. With internet security playing an increasing role in politics, this knowledge has become even more important in a democratic society.

  • Computer Science for Future Leaders

    There’s a great physics course out there called Physics for Future Presidents. For some time I’ve been mulling over what a Computer Science for Future Presidents (and Prime Ministers) would look like.

    Last week I taught an introduction to online safety to a group of political activists (experience report here). Along the way I taught a lot of introductory computer science and saw opportunities to cover even more.

    I’ve taught a number of introductory CS classes that are introductions to programming. Like a lot of computer scientists I appreciate coding as an important tool in CS, but don’t like how so many students walk out of their first (and potentially only) CS class with the idea that CS == programming. Computational thinking classes make for a good step away from this misconception but still don’t cover all the things I’d want future world leaders to know.

    The internet and cybersecurity makes a great way to introduce computing — and to cover what future world leaders need to know about computer science.

    This is what I’d cover in a 12 week course. This course would complement an introduction to programming and the two could be taken concurrently.

    ###
    Computer Science for Future Leaders

    1. Introduction to the course. Searching and sorting, and big O notation. I’d introduce binary and linear search, and insertion, selection, and merge sorts. Motivate searching/sorting as necessary for internet computing (indeed, 25% of the world’s CPU time is estimated to be spent on sorting tasks.) Quick review of logarithms.2. Symmetric key encryption. How to encrypt, some approaches for breaking encryption (build on searching/sorting from last week). Big-O of encryption/decryption algorithms.
    2. Graph theory. Define edges/vertices. How to find a shortest path over a network, minimum spanning trees. Talk about costs on networks, congestion, resilience/redundancy. Talk about where you’d want to eavesdrop on a network for maximum coverage. Big-O of relevant graph algorithms.4. Early communication networks. Talk about how telegraphs worked, how data was encoded. Talk about pre-wireless phone networks and how that data is encoded. Introduce some coding theory: error detection and correction over networks.5. What is a file? Character encoding, numerical representation, file encodings. Code lives in files too: HTML as example. What is a file system?
    3. Midterm. What is a computer? Early computers; command-line interfaces.7. Pre-internet computer networks. Talk about packets, packet routing, packet switching. How routers work.
    4. Internetworking: how we can connect networks together. Internet infrastructure (ISPs, IXPs, etc), TCP/IP, DNS. Who governs the various components of the internet (ICANN, RIRs, etc).
    5. Asymmetric key cryptography. Why it was necessary for the internet to grow in popularity. Whitfield-Diffie, RSA, PGP. P and NP.
    6. Secure internetworking. SSL, HTTPS, TOR, VPNs, etc. Cookies. How internet surveillance and censorship work. Cyberwarfare. Dangers of online/computerized voting.
    7. Social networking. How social network websites work. What is their business model? AI and machine learning on the internet, filter bubbles and other biases resulting from machine learning.
    8. HCI of the internet. Usability issues on the internet. HCI approach to security: who is in your personal network and how can you stay safe?
      The whole course covers a lot of computer science: algorithms, theory of computation, systems, networking, crypto, security, HCI, AI. You could add in a bit on databases if you wanted, too.

    Some big advantages of this approach to introducing computer science are:

    • Students get a more accurate feel for what computer science is and what it’s about than in an introductory programming course.
    • Students see computer science as a human endeavour. It’s history is exposed, as well as motivations for the major stages in its development.
    • Similarly, students see how CS is not value neutral. We discuss topics like neocolonialism in technology development, the role of the military in advancing computer science, how the internet is governed, and how the internet affects politics.* Students learn about computer security and the internet that is useful to their daily lives in a way that empowers them. 
    • Improving the state of our democracy. We need leaders and community members to understand these issues to make informed decisions.
  • Helping women in CS with impostor syndrome is missing the forest for the trees

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    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.

  • CS grades: probably more normal than you think they are

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    It’s commonly said that computer science grades are bimodal. And people in the CS education community have spent a lot of time speculating and exploring why that could be. A few years back, I sat through a special session at ICER on that very topic, and it occurred to me: has anybody actually tested if the grades are bimodal?

    From what I’ve seen, people (myself included) will take a quick visual look at their grade distributions, and then if they see two peaks, they say it’s bimodal. I’ve done it.

    Here’s the thing: eyeballing a distribution is unreliable. If you gave me some graphs of real-world data, I wouldn’t be able to tell on a quick glance whether they’re, say, Gaussian or Poissonian. And if I expected it to be one of the two, confirmation bias and System 1 Thinking would probably result in me concluding that it looks like my expectation.

    Two peaks on real world data don’t necessarily mean you have a bimodal distribution, particularly when the two peaks are close together. A bimodal distribution means you have two different normal distributions added together (because you’re sampling two different populations at the same time).

    It’s quite common for normal distributions to have two “peaks”, due to noise in the data. Or the way the data was binned. Indeed, the Wikipedia article on Normal distribution has this histogram of real world data that is considered normal – but has two peaks:

    And since this graph looks in all honesty like a lot of the grades distributions I’ve seen, I decided I’d statistically test whether CS grades distributions are normal vs. bimodal. I got my hands on the final grades distributions of all the undergraduate CS classes at the University of British Columbia (UBC), from 1996 to 2013. That came out to 778 different lecture sections, containing a total of 30,214 final grades (average class size: 75).

    How do you test for normality vs bimodality?

    There are a bunch of ways to test whether some data are consistent with a particular statistical distribution.

    One way is to fit your data to whatever formula describes that distribution. You can then eyeball whether your resulting curve matches the data, or you could look at the residuals, or even do a goodness of fit test. (It’s worth noting that you could fit a normal distribution as bimodal – the two sub-distributions would be extremely close together! If you can fit a normal distribution to it, this is a simpler explanation of the data – Occam’s razor and all.)

    Another way is to use a pre-established statistical test which will allow you to reject/accept a null hypothesis about the nature of your data. I went this route, for the ease of checking hundreds of different distributions and comparing them.

    There are a large variety of tests for whether a distribution is normal. I chose Shapiro-Wilk, since it has the highest statistical power.

    There aren’t as many tests for whether a distribution is bimodal. Most of them work more or less by trying to capture the difference in means in the two distributions that are in the bimodal model, and testing whether the means are sufficiently separate. I used Hartigan’s Dip Test, because it was the only one that I could get working in R #OverlyHonestMethods.

    I also computed the kurtosis for every distribution, because I had read that a necessary but not sufficient condition for bimodality is that kurtosis < 3. When you do thousands of statistical tests, you’re gonna have a lot of false positives. To minimize false positives, I only used Hartigan’s Dip Test on distributions where the kurtosis was less than 3. I set my alpha value at 0.05, so I expect a false positive rate of 5%.

    Test results

    Starting with kurtosis: 323 of the 778 lecture sections had a kurtosis less than 3. This means that 455 (58%) of the classes were definitely not bimodal, and that at most 323 (42%) classes could be bimodal.

    Next I applied Hartigan’s Dip Test to the 323 classes which had a kurtosis less than 3. For this test, the null hypothesis is that the population is unimodal. As a result, if _p_ is less than alpha, then we have a multimodal distribution. This was the case for 45 classes (10% of those tested, 5.8% of all the classes).

    For the Shapiro-Wilk test, the null-hypothesis is that the population is normally-distributed. So, if the _p_ value is less than the alpha value, we can say the population is not normally distributed. This was the case for 106 classes.

    44 of the 45 classes which were previously determined to be multimodal were amongst the 106 classes which the Shapiro-Wilk test indicated weren’t normally-distributed. In short, 13.6% of the classes weren’t normal, many of which are known to be multimodal.

    For the 86.4% of classes where we failed to reject the null hypothesis, we can expect but not guarantee due to type II error that they are normal. I’ve got a large sample size, and good statistical power. From bootstrapping a likely beta value, I estimate my false negative rate is around 1.48%.

    Bottom line: An estimated 85.1% of the final grades in UBC’s undergrad CS classes are normally-distributed. 5.8% of the classes tested as being bimodal, which isn’t a whole lot more than the false positive rate I’d expect to see (5%).

    Discussion

    I’ve only analyzed distributions from one institution, so you might be thinking “maybe UBC is special”. And maybe UBC_ is _special.

    I couldn’t get my hands on a similar quantity of data from my home institution (U of Toronto). But every U of T class I could test was normally-distributed (n=5). Including classes that I’d taught, where I’d eyeballed the grades, and then told my colleagues/TAs/students that my grades were bimodal. Oops.

    Since I thought CS classes were bimodal, when I looked at my noisy grades distributions, I saw bimodality. Good old System 1 Thinking. Had I taken the time to fit my data, or statistically test it, I would have instead concluded it was normally-distributed.

    I’m currently reading Stephen Jay Gould’s The Mismeasure of Man, and this part stuck out for me: “Statisticians are trained to be suspicious of distributions with multiple modes.” Where you see multiple modes, you’re likely either looking at a lot of noise – or two populations are improperly being sampled together.

    Why are CS distributions so noisy? My colleague Nick Falkner recently did a series of blog posts on assessments in CS classes, and how they’re so truly ugly. And my colleagues Daniel Zingaro, Andrew Petersen and Michelle Craig have written a couple of lovely articles which together paint a story that if you ask students a bunch of incremental small concept questions, rather than one giant all-encompassing code-writing question, you get grades distributions which look more normal. How we assess our students affects what sort of distribution we get.

    Perhaps once we as CS educators figure out better ways to assess our students, our grades distributions won’t be quite so noisy – and prone to miscategorization?

  • A Seven-Step Primer on Soft Systems Methodology

    I’m currently TAing for CSC2720H Systems Thinking for Global Problems, a graduate-level course on systems thinking. In class today we talked about soft systems thinking (SSM), an approach which uses systems thinking to tackle what are called “wicked problems“. I thought I’d outline one approach to SSM, as it’s useful to CS education research.

    Step 1: Identify the domain of interest

    Before you can research something, you should first decide what your domain is. What topic? What system are you studying? For example, “teaching computer science” could be your starting point, as could “climate change”.

    Chances are you’re looking at a wicked problem. Conklin’s definition of wicked problems are that:

    1. The problem is not understood until after the formulation of a solution.
    2. Wicked problems have no stopping rule.
    3. Solutions to wicked problems are not right or wrong.
    4. Every wicked problem is essentially novel and unique.
    5. Every solution to a wicked problem is a ‘one shot operation.’
    6. Wicked problems have no given alternative solutions.Because you’re looking at a domain which doesn’t have a clear definition or boundaries, you’ll first want to immerse yourself in the domain. One trick is to draw “rich pictures“, which are essentially visualized streams of consciousness.

    You should also think about what perspectives you bring into this domain. What biases and privileges do you have going into this? Why are you interested in this domain? What do you have to gain or lose here?


    Step 2: Express the Problem Situation

    A good expression of a situation should contain no value judgments. As a heuristic, ask yourself if it’s possible for somebody to not see your statement as a problem.

    For example: “Global surface temperatures on Earth have been increasing since the late 19th century” is a statement some people may not even see as a problem, whereas “Climate change will destroy our way of life” presents climate change as a problem, rather than as a situation.

    The goal of expressing a situation is so that you can then identify the different ways people would frame this as a problem. If you express the situation as inherently problematic, this will narrow the problem frames that you’ll think of.

    It may take you a few drafts to come up with a situation statement you’re happy with. That’s fine; SSM is an iterative process. You may find in later steps you’ll want to come back here (or to earlier steps) and revise your work.

    Step 3: Identify Different Problem Frames

    How you frame a situation will affect the types of analysis and solutions you’ll come up with. Your next step is to think of the different ways the situation could be framed, before you pick which one to proceed with. I’ll give three examples.

    Situation: A large fraction of undergraduate students fail in first-year CS. 
    Some problem frames:

    1. The context is problematic. Students are overburdened in all their classes, and have a difficult time adjusting to university study.2. The curriculum structure is problematic. CS1 material builds on itself to an extent that other first-year classes don’t. If you fall behind, it can be impossible to catch up.
    2. The amount of content is problematic. We pack too much material into CS1 for students to properly absorb and understand.4. The pedagogy is problematic. We just aren’t teaching CS1 effectively.
    3. The affordances are problematic. We teach CS1 using programming languages which don’t reflect how non-programmers reason about computation (e.g. while loops vs. until loops) 
    4. It isn’t a problem. The failure rates, while high, are consistent with other first-year courses.7. Computer science is inherently difficult for humans to learn.
      Situation: Computer science is not available in every high school in Canada.
      Some problem frames:

    5. If the general population doesn’t learn about CS, they won’t be able to properly participate in a 21st century democracy. Issues of privacy and security are poorly understood but necessary for democracies to address.

    6. Not enough young Canadians are equipped with the necessary computational skills for the workforce.3. CS is generally only offered at affluent, urban, schools. This causes racial and class disparities in access to CS, and in turn, to lucrative jobs.
    7. Fewer girls than boys take high school CS; if more girls could take high school CS it could reduce the gender gap.5. There aren’t enough qualified high school teachers to teach CS in every school.
    8. Schools are underfunded and overburdened.
    9. This isn’t a problem, schools should be focusing on other topics instead! 
      Situation: The percentage of women completing undergrad CS programmes hasn’t changed since the wide-scale creation of women in computing initiatives.
      Some problem frames:

    10. The initiatives are being undermined by the same external forces that created the gender disparity in CS.2. The initiatives are themselves faulty. They reinforce gender norms and the subtyping of “female computer scientist” != “computer scientist”.

    11. The initiatives are having a positive impact, but not in ways this measurement can capture. For example, they improve the personal experiences of the women in the field, but don’t change the numbers.
    12. Without the initiatives, the percentage would have decreased. Before the initiatives, the percentage was decreasing; it has since flatlined.

    Step 4: Study the problem frames and pick one

    At this point you’ll want to start doing a literature review. As you review the literature you’ll find different papers take different problem frames (explicitly or implicitly).

    Each problem frame will lead to different approaches to studying and “solving” the problem. Let’s return to the CS1 failure example:

    Situation: A large fraction of undergraduate students fail in first-year CS.

    1. The context is problematic. Students are overburdened in all their classes, and have a difficult time adjusting to university study. 
    • Possible solutions: increase financial aid, reduce course load 2. The curriculum structure is problematic. CS1 material builds on itself to an extent that other first-year classes don’t. If you fall behind, it can be impossible to catch up.

    • Possible solution: reconfigure the CS1 material to be breadth-first, like they have at Harvey Mudd3. The amount of content is problematic. We pack too much material into CS1 for students to properly absorb and understand. 

    • _Possible solutions: _spread it out amongst more classes, get CS1 in all high schools4. The pedagogy is problematic. We just aren’t teaching CS1 effectively.

    • Possible solutions: use peer instruction and/or pair programming 5. The affordances are problematic. We teach CS1 using programming languages which don’t reflect how non-programmers reason about computation (e.g. while loops vs. until loops) Ultimately for your research you’ll need to pick a single problem frame to work within. At this point you should choose one and justify why you’re picking that one rather than the other ones. Once you’ve picked your problem frame you’ll want to carefully review and analyse the relevant literature.

    Step 5: Arena of Action

    Think about your problem frame like you’re framing a photograph. Who or what is in the foreground? In the background? Not in the frame at all?

    Or, if you see your situation as an arena: who is in the arena? Who is being affected by the situation? Who has the power to change the situation? Who benefits from this framing, and who loses out?

    CATWOE Analysis can come in here: who are the customers/clients? The actors? The transformation process? What’s the world view underlying this? Who “owns” or controls this? What environmental constraints are there?

    When I took a class on policy analysis, some questions we asked were: “Why was this policy adopted? On whose terms? Why? On what grounds have these selections been justified? Why? In whose interests? Indeed, how have competing interests been negotiated?” as well as “Why now?” and “What are the consequences?” (from Taylor et al, 1997, “Doing Policy Analysis”)

    Step 6: Theorize/model the relevant system

    Having identified your problem frame, you’ll want to return to the system at hand. Depending on the problem, you may want to model it and/or theorize about it based on the existing literature.

    Once you have a model, compare it to real world evidence. You may have to design and perform empirical studies in order to accomplish this.

    Step 7: Identify possible/feasible changes to the situation, and take action

    Like it says. Soft systems methodology was created for action research, a style of research intended to create social change. In this paradigm, research is bad if it doesn’t help improve the situation.

    Your goal in doing this research is to identify changes to the situation which are both possible and feasible. You should identify who has the power to enact these changes. Challenge yourself: contact these people and share your findings. Advocate for your proposed solution.

  • 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.”

    Positivism

    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.