I blog a lot about the course that I co-run. One thing i haven't blogged extensively about is the students themselves. I've noticed as the semester goes on, our course becomes more female. We started out with about a 50-50 sex ratio in the room. As we moved through basic Python section of the course and into the "special topics" part, that ratio changed pretty dramatically.
Women came to a lot more sessions than men. Just doing cursory headcounts, by the end of basic Python, about three-quarters of attendees were female. In our session on high-performance cluster computing with TACC, there was one lone dude in the a room full of women (besides Ben, my co-instructor, and Benni, the guy teaching the session). Same story in this week's local BLAST session.
Why do women come to more classes than men?
I have a few hypotheses about what's going on here, and I'd like feedback for how to test these.
Hypothesis one:
Nothing to see here; it's a fluke. My inclination is that this is unlikely to be true. I couldn't find specific stats on the sex ratios in all of the graduate programs that from which students come, but overall, the College of Natural Science skews male. Most of our students are from the Evolution, Ecology and Behavior program, and the Institute of Cell and Molecular biology. These departments have more equal sex ratios, and EEB leans towards female-majority. But I wouldn't expect the aggregate of these departments to produce as strong a female bias as is observed.
Hypothesis two:
There's been a lot made of the confidence gap. [Ed. Note: I don't love the term because the confidence gap is caused by sexism and unequal treatment of women, and it seems like talking about this gap talks around the root cause.] Is it possible that women perceive themselves to be less capable of self-instruction than men?
Hypothesis three:
Do men come to graduate school with more of a computational background than women? It's not a secret that there are fewer female computer science bachelor's earners than male. But we're not instructing computer scientists. We're instructing biologists. And it's far slipperier to get your hands on data about how many biologists receive training in computer science. Research by Barraquand et al. suggests that training in overall quantitative topics is considered inadequate by many ecologists. A look over the getting to know you survey that we did did at the beginning of the course suggests that men and women come to our working group with similar levels of experience. My feeling is, therefore, that this is unlikely to be true. But it's possible that men had an overall higher familiarity with programming as a discipline feel that they need less training. Could be an interaction between this hypothesis and hypothesis two.
Hypothesis four:
Women seek training more because they feel they need it to get ahead. I know, personally, that I've sought out formal training in the past because I felt that I wouldn't be taken seriously, as a woman, unless I had some sort of seal-of-approval verifying my skills. I see no reason that this would be untrue for other women.
Hypothesis five:
Our course environment might be more conducive to women's comfort. Half our instructional staff (but not our guest speaker line-up) is female. I'm pretty outspokenly feminist. We're not scary like some of the computer science people are. Ben and I are good friends, most of the guest speakers are my friends, the (female) TA is my friend. And I'll personally vouch for their character. None of us will grope or sexually harass students (like I've been by a teaching assistant in the course of my computational education). It's been recently suggested that women don't go into tech because the men in tech are jerks. I think there's something to this, and the fact that we aren't creepy jerks might be helping us out here.
I'd like to apply some data to this problem. I've currently got some feelers out for people who study this type of thing. If you know someone who works on this type of research, or are someone works on this type of research, get in touch! I'd love to hear from you.