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Scaling up Women in Computing Initiatives: What Can We Learn from a Public Policy Perspective?

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Scaling up Women in Computing Initiatives: What Can We Learn from a Public Policy Perspective? Scaling up Women in Computing Initiatives: What Can We Learn from a Public Policy Perspective? Elizabeth Patitsas, Michelle Craig, and Steve Easterbrook Department of Computer Science University of Toronto Toronto, Ontario, Canada patitsas, mcraig, [email protected] ABSTRACT initiatives, we tend to evaluate one initiative at a time, using How to increase diversity in computer science is an impor- the beneficiaries of the initiative as the unit of analysis. tant open question in CS education. A number of best prac- In this paper we will be considering initiatives as the unit tices have been suggested based on case studies; however, of analysis, rather than individuals. We present a conceptual for scaling these e↵orts up in a sustainable fashion, it re- framework for classifying diversity initiatives, providing a mains unclear which types of initiatives are most e↵ective in first step toward a policy analysis approach to computer which contexts. We examine gender diversity initiatives in science education. CS education from a policy analysis perspective, adapting McDonnell and Elmore’s 1987 notion of policy instruments, 2. POLICY INSTRUMENTS wherein the initiative is the unit of analysis. We present a In this paper we treat women-in-computing initiatives as conceptual framework for categorizing the di↵erent policy acts of department or classroom policy. This framing of di- instruments by a cross of ‘leverage’ and ‘targetedness’, and versity initiatives allows us to draw on literature from public discuss how di↵erent types of initiatives will scale. We argue policy analysis. To simplify the scope of this paper, when that universally-targeted, high-leverage initiatives are most we talk about ‘diversity’ we will focus on gender diversity important for scaling up diversity initiatives in CS educa- specifically. We will return to the broader diversity issues in tion, with medium-leverage change being a stepping stone subsection 7.1. to high leverage change. We use a broad definition of ‘initiative’: any formal e↵ort to increase female engagement in computing. Some exam- ples of what we mean by initiative, or policy, include: 1. INTRODUCTION Admissions criteria change: changing admissions crite- In the past three decades, a great deal of e↵ort has been ria to focus on ‘non-numerics’ like at CMU [38] put into trying to improve female participation in computer Degree requirement change: having multiple CS1s sep- science. Yet, the numbers in North America haven’t budged: arated by experience level / applications [1] women continue to make up only 18% of CS majors [21]. Curriculum change: using MediaComputation to teach Some e↵orts have had tremendous, sustained results. For CS1 in a context-focused fashion [26] example, Carnegie Mellon and Harvey Mudd have both in- Pedagogy change: randomly calling on students to ensure creased the percentage of women studying CS from around that all students speak equally in the classroom and 15% to around 40% in the span of a few years [1, 38]. overcome a ‘defensive climate’ [24] These initiatives remain unusual, however. While they Sending students to the Grace Hopper Celebration: provide proof that change can happen, they do not provide to foster community amongst female students and ex- a roadmap on how to bring that change to scale. pose them to the ‘real world’ of computer science [1] Scale has become a new focus for CS education [28, 20]: Research opportunities for first-years: to foster early as CS is increasingly taught to a wider audience – especially interest in CS [1] in k-12 school systems – how can we handle the scale? To K-12 outreach: bringing k-12 students to the university look at the issue of scale, we adopt the lens of public policy to expose them to computer science activities, such as analysis: we consider women-in-computing initiatives as acts via a summer camp or day-camp [14] of policy. Researchers who study education at scale – particularly The education policy literature provides us a notion of education policy – often work with units of analysis such ‘policy instrument’: thinking about qualities of policies them- as initiatives, policies, schools, or regions. In comparison, selves, using the policies as units of analysis. The approach in the CS education community we tend to work with in- comes from McDonnell and Elmore’s 1987 classification of dividuals as our units of analysis. Even when we evaluate macro-level policies as being mandates, initiatives, system change, or capacity-building [40]. Permission to make digital or hard copies of all or part of this work for personal or Other policy researchers have classified policies di↵erently classroom use is granted without fee provided that copies are not made or distributed (e.g. [18]); the insight here is that policies themselves can for profit or commercial advantage and that copies bear this notice and the full citation be classified and their classifications theorized upon. on the first page. Copyrights for components of this work owned by others than the As our focus here is on department-level policies – rather author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or than nation/state-level – with a focus on scaling up women- republish, to post on servers or to redistribute to lists, requires prior specific permission in-computing initiatives, we have constructed our own con- and/or a fee. Request permissions from [email protected]. ceptual framework of policy instruments. We classify women- ICER ’15, August 09 - 13, 2015, Omaha, NE, USA. in-computing initiatives by two axes: ‘targetedness’ (how Copyright is held by the owner/author(s). Publication rights licensed to ACM. ˇ broad the audience is) and ‘leverage’ (how deeply the sys- ACM 978-1-4503-3630-7/15/08ÂE ...$15.00 tem is changed). http://dx.doi.org/10.1145/2787622.2787725. 3. TARGETEDNESS people at small risk may give rise to more cases of disease In public health, the Universal/Selective/Indicated (USI) than a small number who are at high risk”[17]. And as many model has proven to be an e↵ective conceptual tool for form- diseases are contagious, universal initiatives can improve the ing public health initiatives [51]. In this model, initiatives resilience of the whole population. are categorized by the intended audience: universal strate- Furthermore, as universal initiatives target the whole pop- gies are aimed at whole populations; selective strategies are ulation, they provide a means of reaching at-risk individuals aimed toward at-risk groups; and indicated strategies are who are not in contact with institutional services [17]. aimed toward individuals displaying signs of the condition in question. 3.2 Selective initiatives To give some more concrete examples from suicide pre- In comparison, selective initiatives target a population vention: known to be underrepresented in CS; they specifically and explicitly benefit that group, and provide them with tar- Universal initiatives: restricting exposure to suicide con- geted support to ‘level the playing field’ with dominant groups tent in mass media, adding barriers on bridges, and in CS. Examples include: restrictions on pharmaceutical dispensing [17] Selective initiatives: selective initiatives here include sui- A CS department makes a mentorship programme avail- • cide prevention centres and hot-lines, community or able to all female students school suicide prevention programmes, and programmes Departmental women-in-CS clubs • for veterans and military personnel [17] Giving the opportunity for female students to go to Indicated initiatives: training general practitioners to spot • the Grace Hopper Celebration warning signs in patients and how to talk to patients Outreach initiatives for school-age girls about suicide, postvention, and crisis hot-lines [17] • Scholarships for women in CS Here we’ll extend the USI model from public health to an • education setting. We will refer to the spectrum it repre- Many selective initiatives in public health – suicide-related sents as ‘targetedness’: with universal initiatives being less or otherwise – have been found e↵ective. Meta-reviews have ‘targeted’ and indicated ones being most ‘targeted’. ‘Tar- noted that long-term selective initiatives tend to be more getedness’ refers to how wide the audience is, not which successful than short-term ones. Selective initiatives need audience is being targeted. to be culturally and contextually appropriate to the audi- ence(s) in order to be e↵ective [17]. Certain selective initiatives have been noted as having po- 3.1 Universal initiatives tential harm – for example, being seen associated with a In an education context, our idea of “population” di↵ers. group for a stigmatized disease/condition. Like in public A“population”can be a whole classroom of students, a whole health, stigmatization has the potential to be an issue in ed- CS department or school – or even the general population ucation. Audit studies have found that job candidates asso- of a country. The key notion is that universal initiatives are ciated with affirmative action (which targets specific groups) carried out without regard to population target groups or are perceived as less competent than identical job candidates risk factors. Examples of diversity initiatives in CS educa- without those associations [31]. This e↵ect is strongest when tion which are universal include: the job candidate’s competence is ambiguous [31]. Some A CS department makes a mentorship programme avail- qualitative studies of women in science have also noted that •
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