Being included

I recently finished grading a set of design reports from my Software Design class.*  For this particular project, they were to propose a redesign for our department web pages, including mockups and a summary (and rationale) of their design changes.  The reports were very well done overall, and the students had some great ideas which I plan on bringing to the department.

As part of the redesign process, the students had to identify the potential audience of the web pages and come up with personae representing different types of visitors from this audience (students, faculty from other schools, alums, etc).  Traditionally, personae are given names/identities, to better facilitate telling “stories” (scenarios) about how they will use the site.

What left a lasting impression for me was that the students incorporated both male and female personae into their reports.  There was only one group that had all male personae.  The rest of the groups had at least one, and in general about half, female personae.  (A few groups had a majority of female personae.)  And these were not limited to stereotypical examples like “the mother of a prospective student”—CS alums, current majors, and CS professors here and elsewhere also showed up as female personae in the reports.

Now, I had no hints in the assignment prompt other than “use professional language” in terms of what language they should use.  And I’m guessing what I’m seeing is the result of more attention paid across the board to using inclusive language in writing—in which case, I’m heartened that my students have so naturally incorporated these lessons into their writing.

But I wonder how much of this is influenced by the fact that the professor standing in front of them day after day is a woman.  Or that they have two female role models now in the department.  Or that there are more women students in our classes and declaring CS majors.  (Although it’s worth mentioning that this class is overwhelmingly—89%—male.)  We don’t yet have critical mass, but nor are the numbers of women tiny either.  Women are visible, and they are occupying roles traditionally occupied by men.

The thing is, language is as much of a signal as to “who belongs” as, say, the presence of a Star Trek poster, or piles of computer parts, or plants and coffee makers in a lounge.  Language is a form of place and of space.  The fact that my students see women as natural occupants of the CS space, as evidenced in their choice of language, is huge.

It’s a small victory, but I’ll take it.

* Yes, we are still in session here.  Tomorrow is the last day of classes, so the end, at least, is in sight.

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To change or not to change—should that be the question?

I just got back from the annual NCWIT Summit, in NYC.  Carleton recently became a member of NCWIT’s Academic Alliance (yay!), and one of the perks responsibilities of membership is attending the annual summit.  The summit is a chance for the members of all the alliances (academic, entrepeneur, workforce, K-12, etc) to get together, network, share best practices…and hear fabulous speakers.

This year one of the speakers was Joshua Aronson, who’s done a ton of excellent work on stereotype threat.  (He and Claude Steele coauthored the original, seminal paper on stereotype threat.)  The talk was excellent—he had a lot of great anecdotes (and is a good storyteller) and ended with some hopeful and promising ideas for mitigating stereotype threat.

However, he told one story that, frankly, still infuriates me.

In 2004, Stricker and Ward wrote a paper on a study sponsored by ETS, the company that is responsible for the AP exams.  The study tested whether moving the usual questions on gender, race, etc. to the end of the AP AB Calculus exam, after the students finished answering the questions, would raise the scores of underrepresented groups on this exam, instead of asking these questions before the exam.  The idea is that if stereotype threat was an issue, then moving the questions on race and gender to the end of the exam should have a positive effect on test scores of those most likely to be affected by stereotype threat.  The study found no statistically significant difference in scores.

In 2008, Danaher and Crandall reexamined the study’s data, and found quite different results.  They found that the criterion applied in the original analysis was too conservative.  Changing the timing of the question in fact had a great effect on women’s performance, and that as a result 4700 more women would have earned scores high enough to earn placement credit for AB calculus.

Now, you would think that the ETS would have a vested interest in making these tests as fair and equitable as possible.  After all, these tests are supposed to test knowledge about a subject, so why wouldn’t we want to make the test conditions as fair and unbiased as possible?  So why not move the questions on demographics to the end of the exam, especially if there’s no good reason why they have to be asked at the beginning (and many reasons why they shouldn’t be asked at the beginning)?  But even today, ETS has failed to make this simple change in the exam.

One of the messages/themes of the summit this year (and maybe in most years—this is only my second year attending) is “small changes count too”.  One or more speakers explicitly mentioned that a small deed is better than no deed, or that small deeds start change, or some variation of this message.  Changing the timing of the gender/race questions is a small deed.  At worst, it has no effect on scores.  At best, it can have an important and measurable effect on reducing stereotype threat.

So why not make the change, ETS?  You’ve got so little to lose and so much to gain.   As an organization invested in having more students participate in AP examinations and AP courses, why not also invest in a change that will possibly remove some subtle, unconscious, but real barriers to the demonstration of knowledge?  What good is having more people at the table if those people aren’t performing up to their best level—particularly when you can make a change that might very well remove this performance barrier?

Why not, ETS?

References:

Steele, Claude M.; Aronson, Joshua (1995), Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology 69 (5): 797–811.

Stricker, L. J. and Ward, W. C. (2004), Stereotype Threat, Inquiring About Test Takers’ Ethnicity and Gender, and Standardized Test Performance. Journal of Applied Social Psychology, 34: 665–693.

Danaher, K. and Crandall, C. S. (2008), Stereotype Threat in Applied Settings Re-Examined. Journal of Applied Social Psychology, 38: 1639–1655.

The “dangers” of escalating enrollments?

Eric Roberts, a professor of computer science at Stanford and someone who spends a lot of time and energy thinking about, improving, and researching CS education practices, wrote a guest post at the Computing Education blog addressing the wildly increasing enrollments in university-level CS courses.  The post is very interesting and thought-provoking, but I’ll admit I started reading it with a bit of unease.

The demand for CS courses today is interesting, because as Roberts points out, it’s not just in the Intro course.  It starts there, for sure (Stanford, like us, is seeing record enrollments in Intro CS), which makes sense—the economy is not all that hot right now, students want to make themselves more marketable, and finally people are realizing that knowing at least a bit about technology is a Good Thing.  But that doesn’t explain all the demand, because the record enrollments extend “up the stack”, as it were, into the upper-level courses:

What my colleagues and I are seeing today is entirely different [from what we saw during the dot-com bubble in the late 90s]. The students who are now inflating the ranks of CS106A are, it seems, deciding to take a computer science course as a way of bolstering their credentials before they emerge into a weak economy. Most have majors in other areas but recognize, probably correctly, that having programming skills will likely increase their chances of gaining employment in their own field. A surprising number of those students, however, once they get into our introductory courses fall completely in love with the material and continue on to double the size of the downstream courses in the curriculum.

This explains what we are seeing here, too—our 300 level courses often have enrollments in the high 20s, and one of our 300 level courses this spring (Data Mining) actually filled to capacity before registration ended!  And this is translating into majors—32 so far in the sophomore class, with more expected due to double majors.  Our students are falling in love with CS!

So how do we keep the love alive?  How do we make sure that this is not another bubble?  How do we sustain the interest in CS and, at the same time, support these larger numbers of students, so that they get the quality CS education they deserve?

And this is where the “nervous” part I mentioned comes in.  As Roberts points out in the post, the computing field saw a similar rise in demand in the early 1980s, followed by a precipitous drop in interest.  In a 1999 SIGCSE essay, Roberts discusses a bit of what happened:

At some point in the 1980s, these strategies [to deal with the demand] proved insufficient, forcing departments to restrict demand by imposing limits on enrollment. Some institutions attained these limits by setting strict quotas on the number of students who could major in computer science or by requiring extraordinarily high GPAs to declare computer science as a major. Others achieved the same effect without formal limitations, simply by making the introductory courses so difficult that relatively few students would continue in the field.

…

Students in the mid 1980s did not decide not to major in computer science but were instead prohibited from doing so by departments that lacked the resources to accommodate them. Given the pressures departments faced at the time, these restrictions may well have been necessary. Moreover, they did, in the end, mitigate the crisis. They did so, however, at an enormous cost. At a time when industry needed more people to sustain its momentum, universities were forced to cut back. The flow of students collapsed, and industry was faced with a shrinking labor pool. Given the complexity of any economic system, it is usually impossible to prove causality, but I have believed for some time that the crisis in academic computer science during the 1980s contributed significantly to the industrial decline at the end of the decade.

The paper goes on to discuss why this is bad for various constituencies, but the biggest deleterious effect?

Enrollment limitation will almost certainly have a disastrous effect on the diversity of the undergraduate computer science population. Students from weaker school systems and those who have not had the opportunity to work with computers at home will have much more trouble with introductory courses designed to act as filters for a limited- admission major. Similarly, studies have documented the fact that women are likely to underrate their own abilities with respect to their male counterparts [16]. Faced with a highly competitive admissions process, women are more likely to choose other options in selecting a major. From 1986 to 1991, the number of men graduating with bachelor’s degrees in computer science dropped by 34 percent, while the number of women declined by 51 percent [2]. (emphasis mine)

And this is why I am nervous.  I am hoping that we’ve learned our lessons from the 1980s and that we, as a CS education community, will find more productive and positive ways to deal with the demands on our limited department resources than imposing quotas and re-adopting the “weed-out” mentality that I hated so much as an engineering major.  It is more important than ever, at this point, that we continue the practices that attract all comers to our major, and that we continue to refine our practices in retaining a diverse population in our classes and in the CS major.  It is most important that we do this in times of wealth, so that we can be proactive instead of reactive and build upon the strength of our numbers.

I hope the CS field is up to the challenge!