I’m teaching an interdisciplinary course this semester and I’ve been struggling somewhat to figure out how to bring my discipline into the mix. We conducted a survey and will analyze the results. As I was thinking about what we were going to do with the survey, I made a big, “Doh!” noise, thinking, of course, that’s where my discipline is–the data. I love data, and one of my favorite things to do with students is show them how to store and use data. Data was a huge part of my dissertation and I spent a large chunk of it analyzing all the data I had at my fingertips, which was mostly text, word counts, and link and hit counts. Data always reveals something and I love the way computing helps to show pathways through what the data reveals.
In just putting the survey together, our students have already realized that data isn’t perfect, that data alone can’t reveal everything about why humans do what they do. And even scientific data is messy. So what I’m hoping to do is show them some computationally sound ways to slice our dataset (or other datasets), but then talk about what data does and does not tell you. Most of our readings have basically been an analysis of data. Many students have pointed to gaps in the data. For example, in readings about couples and household chores, there was no data about homosexual couples, which would be an interesting comparison to make with heterosexual couples.
I’m actually excited about the possibilities of having students analyze data and realize that a lot of disciplines need data and computation applied to that data in order to do most of its work. The things we’ve read read like a story, but that’s after a human, using computation, has pieced together a story out of the data at hand. That is hard, important work, facilitated by machines, but still very human and hopefully very appealing to those who may not be choosing computing as their main field. It’s a very real application of computing to other disciplines, and that makes me very excited, indeed! (yes, I’m a geek)