Miriam Posner's Blog

Over the last few years, enrollment in my Introduction to Digital Humanities class has been trending steadily upward, as has enrollment in the minor itself. Last spring, we had an unexpected surge in enrollment in the minor, and many of those students needed to take DH101 right away. We had to scramble a bit to accommodate everyone. After considering a few possibilities, we more or less doubled the size of our Intro class, from 45 to 88 students. We were fortunate to enlist an excellent T.A., Dustin O’Hara, to teach two sections, and my fabulous longtime co-conspirator, Francesca Albrezzi, took the other two. (We have lectures twice a week and section once a week.)

Even with the expanded class size, we had to turn lots of people away; I suspect we could fill another DH101 class in the spring, if we had the faculty bandwidth to teach it.

This was my first time teaching a true lecture course. In previous versions of DH101, I’ve been able to alternate between dispensing information and turning discussion over to the students. While we still had discussions in the larger DH101, I could no longer pretend this was a seminar.

I expected the large class size to be a challenge, but I think the bigger challenge was the classroom itself. We were lucky to find a room at all, given how late we transitioned to a larger class size, but we were stuck with a very conventional lecture hall, with bolted-down seats in immovable rows. It at least had modern AV equipment, but the room was a significant challenge. In my previous classroom, students’ seats were arranged in 10 or so group tables, so it was easy to alternate between hands-on work and all-eyes-up-front lecturing. Now we had no choice but to sit lecture-style.

I did what I could to ameliorate the situation. I was able to reserve the Young Research Library main conference room on a few occasions, which gave us a chance to work more collaboratively. And I did continue asking the students to check in, share work with each other, and discuss issues in small groups in the lecture hall. But the space just didn’t really lend itself to that kind of thing. This was a real bummer for me, and probably for the students, too.

The classroom arrangement actually set us back significantly in terms of technical skills, too. I wasn’t really comfortable asking students to learn technical stuff when I couldn’t circulate freely in the classroom to see how they were doing. I don’t think a lecture hall is a good environment for learning new skills on your computer, since it’s so easy to get stuck and have no way to signal for help without stopping the entire class. So technical tutorials had to be reserved for section, for the rare occasions when I could reserve the Library conference room, and for a few at-home lessons. As a result, I wasn’t able to teach the students as many skills as I have in years past.

I also struggled to check in with students as much as I’ve been used to doing. Their group project is always really challenging for them, and every project is very different. Since I’m the one who picks out the datasets, I usually like to work at least a little bit with every group. But with so many students, I had a hard time devoting attention to everyone. The result was more confusion about the assignment and expectations than in previous years, and a couple of group meltdowns. Everyone pushed through and got to the finish line, thanks in large part to the TAs’ hard work, but it was more stressful than it needed to be.

The students’ final project showcase this week reassured me that, yes, they did learn what I wanted them to, and, yes, they did learn how to do serious research and think critically about data. I loved hearing them explain what they did and how they overcame challenges, and I was really excited to hear their newfound confidence in discussing technical matters. Still, as always, it’s my errors that stand out to me.

If the class remains this size next year (and I’m still the one teaching it), there are a few things I’d do differently.

• Rethink the final assignment. This is tough, because I’ve loved giving them “real” data, and I believe they benefit from the intense labor of making meaning from messy, incomplete, but important datasets. But I’m not sure it’s realistic for me to assemble and augment this many datasets every year. And I worry about the groups getting the attention they need to complete this very complex project when there are so many people to check in with. The alternative that makes sense to me is some kind of digital portfolio, in which students create their own examples of multiple kinds of digital work and surround it with critical commentary.
• Undecorate the Christmas tree a little bit. As the years have gone by, I’ve tossed more and more assignments in the syllabus. I don’t think the class is more work, necessarily, but there are a lot of things to turn in and a lot of dates and assignments to remember. It’s too much. I think I could cut the blog post assignments down to just a few and simplify the final project a lot.
• Think about asking students to complete technical modules at home. I usually like to be with students when they’re learning a new technical skill, but that wasn’t always possible. On a few occasions, I had students walk through (very carefully written) tutorials themselves at home, and they seemed to do OK. I think I could do more of this, as long as I’m cautious.
• Get a different classroom! I don’t think we actually have a great classroom for a group this size at UCLA, but what I imagine would work well is a large room, with lots of space for my TAs and me to circulate, and multiple large tables where students can sit in groups. Multiple screens would be awesome, so that students could quickly draft and share work, but honestly, I’d happily take a large, empty room with tables and chairs, preferably one that we don’t have to set up and tear down every time (ugh).

Other miscellaneous thoughts about this year’s DH101:

• As part of their annotated bibliography, each student needed to not only write a blurb about each of their sources, but actually obtain the book or article and submit a photo of themselves holding it. We called those “shelfies.” I’m just tired of reading book summaries that are obviously pulled from the snippets students could read on Google Books. This seemed to work really well. Students STRUGGLED to find their sources, as I expected, and waited too long, as I expected, but a number of students told us that this was the first time they’d located or checked out a book in their college career. As we did last year, we held a “research-a-thon” to help get them going on this, and while I made a mistake by holding the event during midterms week, the librarians and I were able to personally escort a number of students up to the stacks and help them read a call number.
• Students took to network analysis more than they have in years past, perhaps because a number of them were simultaneously taking an SNA class in the Sociology department. I’m happy with the lesson plan I’ve developed to introduce network analysis, which uses a questionnaire about their favorite books, movies, and musicians to develop a homophilic network graph to show how they’re all connected. (I recorded last year’s network analysis lecture and you can see it here.)
• For the last couple of years, it’s been clear that the hardest thing about the final assignment for my students is getting started — understanding what kind of work is necessary to start asking questions of a dataset, and how to alternate between secondary research and data analysis. The DataBasic suite really helps with this, but I think they could use step-by-step instructions to get started. Perhaps I’ll take that on at some point.
• I just did not have the wherewithal (or the funding) to schedule a pizza-dinner hackathon, as I’ve done in previous years, but I found a simple alternative that they seemed to appreciate. I convened an evening meeting to which each group had to send at least one representative and checked in with each group that way. Then, at the same time every week, I invited each group to sign up for dedicated help with me. It worked well and allowed me to work intensively with a few groups.
• You probably guessed this, but with a lecture this size, you need to make every announcement multiple times and send email followups, and even then, students will plead total ignorance.
• For the last few years, I’ve started off the class with a reading from Hayden White, about the essential unknowability of history. This year I switched it up and had them read the first chapter of Michel-Rolph Trouillot’s Silencing the Past, in part because Trouillot explicitly deals with power and race in ways that White doesn’t. They really struggled to understand Trouillot, but it seemed to make an impression on them, too.
• Of the DH projects we examined together, the one they all seemed to like the most was Gabriela Aceves Sepúlveda’s [Re]Activating Mama Pina’s Cookbook. I think they liked its consideration of the materiality of data, the questions about what “counts” as data, and the beautiful design. Also, partly because so many of my students are people of color themselves, they appreciate it when I can pull in projects from and about other people of color.

The quarter is off and running again at lightning speed. At UCLA, we’re on the quarter system, and things move fast — just 10 weeks to get through all your material. I’m teaching DH101 again this year, and, as usual, it’s a race against the clock. The profile of my students changes a bit every year, but the typical student who enters my DH101 classroom has facility with Word, PowerPoint, maybe Excel, maybe some of the Adobe suite, but not a ton of other computer stuff. By the end of the quarter, my goal is to get them working with and thinking critically about structured data, data cleaning, data visualization, mapping, and web design.

I’ve written about this before: working in groups, my students are assigned a dataset at the beginning of the quarter. They learn how to work with it as the quarter progresses, doing a lot of secondary contextual research, interviewing an expert about it, manipulating the data, and finally building a website that makes a scholarly humanistic argument with the support of the data. You can see the mechanics of this on my course website.

People often ask me about the data I use, and indeed, that is a story in itself. I have 88 students this year, and since I don’t like any group to have more than seven people in it, I have 12 groups, each of which needs a dataset. (Really, some of them can share the same dataset; I don’t know why I get weird about this.) And they can’t just use any dataset. In fact, most of the data out there is inappropriate for them.

Here is what I look for in a dataset for my students:

1. It has to be a CSV (or able to be wrangled into a CSV). My beginners want to be able to double-click on their dataset and see…something that they can work with. CSVs are great because they open in Excel, which is familiar to most students and allows them to immediately start doing things like filtering and simple manipulation. Plus, you can drop a CSV into almost any visualization tool. I can use a relational database, but I usually just give the students the spreadsheet that results from a query, since I just don’t have time in the quarter to teach them about more complicated data structures. Likewise, if a dataset is XML, I’ll just flatten it. But I prefer not to have to deal with this because, like I said, 12 datasets.
2. Around 2,000 records is ideal. Here’s why: I want the dataset to be big enough that it’s too labor-intensive for the students to manipulate it by hand, but not so big that it breaks Excel. Really, I can work with bigger sets, too, but students do tend to get very anxious about working with datasets that big. Any number of fields is fine (actually more is better) because students understand fairly quickly that they can choose which fields to examine.
3. It has to be…humanities-ish. You and I probably know that one could make a humanities argument about municipal water data, or public health information, but it takes a little bit of sophistication to get there. The most “natural” kind of analysis for these kinds of datasets would be urban planning or public health kinds of questions, and it’s too difficult for me to push students toward the kind of open-ended humanities questions I want them to pursue. It’s far easier if the data is about art, books, movies — subjects that are the traditional province of the humanities.
4. It’s nice if it’s something they care about. I have confidence that my students will eventually become interested in any subject, once they really dig into it, but I can forestall a lot of grumbling if I can give them a dataset that’s immediately compelling to them. Things they like: fashion, food, performance, books from their youth, cartoons, comic books, TV, movies.

You can see this year’s datasets at the bottom of this page. I do not just give my students their datasets in raw form. I cut the sets down to an appropriate number of records, if necessary, and then I give them the dataset along with a “project brief,” which contains:

1. Information about the provenance and composition of the data.
2. The name and contact info of an expert on that subject who has agreed to allow my students to interview them.
3. The names and contact info of librarians who can help them.
4. The name and contact info of UCLA’s mapping specialist.
5. Two or three secondary sources to get them going on their research. I also teach them how to citation-chain.

Here is an example of a “data package,” with the contact info removed.

If you’re thinking this is kind of an absurd amount of work for the instructor, you’re right. I really feel the students need this apparatus around their dataset, but I end up spending a good chunk of my summer hunting down data, persuading friends (and strangers) to serve as subject experts, and researching secondary sources.

Even with all of this scaffolding, students get very anxious about the project assignment, just because it’s so new to them. I’ve learned to expect it, to warn them that they’ll feel anxious about it, and to reassure them that if they’re hitting project milestones, they’ll get to the finish line on time, even if they feel at sea.

Sorry for the dashed-off blog post; I’ve been meaning to write about this for some time and finally had a few (just a few!) minutes!