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.

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 Scholarship
  All 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 and peer instruction in their class, and randomly calls on students in a structured fashion -- 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.

Bonuses and Software Projects

At today's CS Education Reading Group, one of our group members led us through an exercise about group work from "Students' cooperation in teamwork: binding the individual and the team interests" by Orit Hazzan and Yael Dubinsky.

It's an in-class activity to get students thinking about how they work together in software projects. Students are given a scenario: you'll be on a software team. If the project completes early, the team gets a bonus. How should the bonus be allocated?

1. 100% of the bonus should be shared equally
   
   
2. 80% should be for the team to share; 20% should go to the top contributor
3. 50% team, 50% individual
4. 80% team, 20% individual
5. 0% team, 100% to the individual who contributed the most