{ interventions }

  • Categorizing Interventions: Adapting the USI Model to CS Education


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

    Explicit and Implicit Interventions

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

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

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

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

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

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

    _4.2 Preventive interventions

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

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

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

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

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

    A USI Model for Diversity Initiatives in Education

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

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

    • A whole CS classroom using pair-programming

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

    • Women in CS conferences/celebrations

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

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

  • 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.

    Somebody noticed that the nearly all the fish you see these days are either the white-coloured fish species, or the yellow-coloured fish species – the black fish and the red fish, for instance, are doing about as well as the frogs.

    But since the lake is now 75% fish, you’re really more concerned about the frogs. So, you put your efforts into releasing more frogs into the wild. You construct some frog-friendly spaces. You stop by and give them some canned flies to eat.

    The problem, though, is that the frogs are still gonna keep dying if the lake is polluted. The frogs are an indicator species – amphibians are more sensitive to environmental toxins. And that pollution is having the same effect on the red and black fish species, if you were paying more attention to them. The two fish species that are less affected happen to have some adaptation that is protecting them.

    If you start to remove the pollution from the lake it helps both the fish and the frogs. Sure, it has a larger impact on the frogs, but it also helps the fish. Some species and subspecies of fish were also getting disproportionately hurt by the environmental toxins too, and it’s good for them.

    If you want to fix the ecosystem, you have to fix the ecosystem for all the species involved. A culture polluted with the stereotype of computer scientists being asocial nerds in basements hurts the whole environment. It pushes women and other minorities away from going into the field, thinking it’s not where they can belong.

    For real change to happen in CS, we need to move past band-aids like Women in CS clubs and mentorship programmes for African-Americans. These initiatives have a role to play in the solution, but they can’t do it alone: We need to get rid of the pollution. Let’s work on cleaning up the lake for our whole ecosystem: it’s not about the frogs, it’s about the lake.

    ### How Do We Clean Up The Lake?

    I’m doing my PhD on diversity in CS. So far in my research, I have come to a hypothesis about diversity initiatives.

    Here’s my assertion: _A women in CS initiative is more likely to be successful in the long-term if that _particular_ initiative also helps other minorities (e.g. aboriginals, Blacks, Hispanics, people with disabilities, first-generation university students)._

    And by “particular initiative” I don’t mean “we started a mentorship programme for women in CS, somebody could make a mentorship programme for students with disabilities in CS” – I mean “we started a mentorship programme that all students can participate in”.

    Why is this important?

    First off, well-intentioned diversity initiatives can sadly be counterproductive:

    By calling out women for extra help, it reinforces that they are different, which reinforces their otherness. It can also trigger stereotype threat. Overtly feminine role models hurt girls’ interest in science – these women are seen as being unrealistic, and are not mentally categorized as scientists When teaching/recruiting, women speaking about CS are more likely to say the field is “hard” – and perceived difficulty pushes prospective students away from CS Diversity programmes cause people to take discrimination cases less seriously
    These types of initiatives are also prone to survivorship bias
    The empirical research we have on hand about what has long-term benefits shows us that the tricks that help women help everybody – and unintuitively, many of these are very small changes to make!
    Using assignments in first-year CS that demonstrate how CS contributes to society
    Value-affirming exercises to decrease stereotype threat Mentorship programmes for all students
    Exposure to non-traditional but non-counterstereotypic role models Ditching the nerd stereotype when depicting CS in media
    Shifting students from fixed mindsets to growth mindsets– for example, by talking about the struggles that great scientists have encountered and how they surpassed them – rather than focusing on their achievements Improving parental leave – having men and women share parenting more equally keeps more women in CS
    * Letting students know that it’s normal to worry you won’t fit in at first, but that it will go away with time
    Improving diversity for all underrepresented groups makes it easier for the other ones to show up – you’re removing pollution from the lake. Women are more likely to feel at home in a CS department that is multiracial – the more you pry CS away from being a white-and-Asian-boys club, the more you help all underrepresented groups join the community.

    We already know a lot about why women drop out or aren’t interested in CS. Let’s target those underlying reasons and fix them for all students, without framing them as women’s issues or ghettoizing women. It’s not about the frogs – it’s about the lake.