My DH101 class this year was my biggest yet, with 45 undergrads. I suppose that’s not huge compared with many other classes, but DH101 is very hands-on. I am fortunate enough to have a TA, the awesome Francesca Albrezzi, who runs separate weekly labs. Still, I often have to teach students to do technical things in a large-group setting, and the size of the class this year prompted me to rethink how I do this.
As I see it, many of my students’ biggest problem with computers is their own anxiety. Obviously, I have a self-selecting group, since I teach a class with “digital” in the title, but even so, many of my students tell me that they are just “not technical.” Many of them are so convinced of this that they see any failure to get something to work as confirmation of what they already knew: they’re just not good with computers.
And since this is UCLA, the vast majority of my students do not fit the stereotype of the Silicon Valley programmer. This is awesome for the class, since we have so many different voices in the room. But it also puts many of my students at risk for stereotype threat, in which students’ performance suffers because they fear their mistakes will be seen as representative of their entire race or gender.
I’ve seen a version of this happen in workshops countless times. The instructor issues directions while students try to keep up at each step. Some students accomplish each step quickly, but some students take a little longer to find the right menu item or remember where they’ve saved a file. No matter how often you tell students to please interrupt or raise a hand if they need help, most students won’t do this. They don’t want to slow everyone else down with what they’re sure is a stupid question. Eventually, these students stop trying to follow along, and the workshop becomes, in their minds, further evidence that they’re not cut out for this.
I’m teaching Introduction to Digital Humanities for the third time this year, along with Francesca Albrezzi, my wonderful two-time teaching assistant, and I’m really enjoying it. It’s a challenging but rewarding class, with 45 students, a 10-week quarter, and a large number of moving parts. I reworked the syllabus quite a bit for this iteration, and I thought it might be useful to talk about what I’ve done differently and why.
As I’ve taught through the class a few times, its purpose and value have become more clear to me. My version of DH101 is about developing a humanistic attitude toward data. To me, that means the ability to hold in one’s mind simultaneously the value of any particular dataset and its inevitable poverty, compared with the phenomena it purports to describe. I want students to be able to “work” with data — that is, to analyze, visualize, and map it — but also to retain a perpetually critical, interrogative stance toward it.
In the service of this goal, I’ve completely rewritten the students’ final project assignment. The previous assignment, which I first inherited and then adapted, was for students to work in groups to build Omeka projects on topics of their choice. This had the benefit of exposing them to the demands and complexity of Dublin Core metadata, but I felt that the students were spending too much time describing objects and not enough time working directly with data. Since Omeka has no real export function, they weren’t able to do much with the metadata they were creating, besides build exhibits.
(Mark and I were able to teach our classes at all in large part because of the generosity of the scholars involved in the Selfies Research Network, to whom I owe a big debt of gratitude.)
Mark’s class generated a ton of publicity, and because he mentioned my own class, I rode Mark’s coattails a bit as we got mentioned in the New York Times, the LA Times, and elsewhere. Of course, Mark and I knew that the only reason our classes were getting any press was so that people could talk about how ridiculous a selfie class is. But it was still fun, and we tried to inject as much substance as we could into the conversation.
Meanwhile, the ever-awesome Liz Losh took the time to really dig into the substance of my class in this excellent post on the DML blog; I was really honored to be interviewed.
I got an interview request for another outlet, and since the article seems not to have seen the light of day, I thought I’d just post my responses to the interviewer’s questions here.
Incidentally, I don’t really take my own selfies, not because I disapprove of them, but because I’m really bad at it. Much respect to people who can do it well!
What enticed you to teach a class centered around the selfie?
The class wasn’t entirely centered around the selfie. It was about the experience of being a young adult in the digital age and, more broadly, how we should think about the relationship between technological and cultural change. I wanted to teach this class because I’ve heard a lot of generalizations about millennials, both in the media and from people I know, and I felt that many of these characterizations didn’t accurately reflect the complicated, diverse people I encounter in the classroom at UCLA. I wanted to submit those generalizations to rigorous scrutiny, to see whether they held up.
I also noticed that every time I mentioned social media or online culture in the classroom, students were really eager to chime in with their own experiences. I thought it would be fun and interesting for us to carefully study something they care so much about. I also have a sister who’s 21, so I felt a personal investment in countering some of the more pernicious stereotypes about young adults.
What insights and observations have you gained regarding the relationship between students and social media?
My students had a ton to say about social media and its relationship to youth culture. One thing I found most interesting is how worried they are about social media’s effects on their attention spans and relationships. That makes sense, since they’re hearing the same news stories and media messages about millennials that we are! But they’re thinking very hard about technology and social change; no one should assume that just because a young adult has her eyes on her phone, she’s not also self-aware and thoughtful.
Can you give an example of an assignment for the course?
Students’ main project was a digital ethnography (meaning an in-depth study of a particular culture) of an online community. I asked them to immerse themselves, and in some cases participate in, an online community of their choice. We had a couple Tinder papers, one on Yik Yak, and a few on Instagram. Students were surprised at how hard it was! We spent a long time talking about how to be an ethical, honest, and diligent participant-observer.
Based on what you’ve seen among students, are there specific aspects that constitute a typical selfie?
I think it really depends on context. Selfies can have different meanings, depending on who’s taking them and for what purpose, and often you’ll find people consciously imitating or exaggerating elements of the “typical selfie” for ironic effect. For example, many teenage girls will offer up an exaggerated “duckface” to the camera, in a conscious and ironic imitation of the “typical selfie.” Just as any portrait can, a selfie can mean many different things, and one has to be very alert to its context when one’s trying to suss out the meaning of any particular image.
Outside of classroom purposes, do you condone taking selfies? If yes, how do you justify a selfie as something more than an act of narcissism?
I don’t really think it’s my place to condone or not condone any form of participation in a visual culture. Community, as we all know, means a lot to people, and taking selfies is one way that some people participate in a community. I think we should also be very alert to what is connoted by the word “selfie.” As the term is popularly used, it’s closely associated with teenaged girls, who have frequently been the object of scathing ridicule in American culture. I think of selfie-opprobrium as somewhat akin to people’s annoyance at vocal fry: both phenomena are associated with teenage girls, and both suggest a degree of annoyance (perhaps even fear) at girls’ temerity in entering the public sphere.
What do you hope students carry away from the course?
On our last day of class, I asked students what they’d remember about what we learned. They all agreed that “It’s complicated!” — which is also the title to danah boyd’s recent book, which we read in class. What boyd means, and what my students meant, is that you can’t assume that all online youth culture is one thing, or that every young person experiences life online in the same way. Phenomena that look very similar to outside observers can turn out, on closer inspection, to have very complicated and multilayered meanings. Young people — like all human beings — are complicated, diverse, and multifaceted. Sweeping generalizations about them are unhelpful and usually wrong.
They also said they’d remember our discussions about the need to “hustle,” by which we meant the reality of labor in the twenty-first century. Students carry unprecedented educational debt these days, even as the likelihood of them owning their own homes, or even attaining the same living standard as their parents, is lower than it has ever been. Steady jobs, the kind with pensions and benefit plans, are becoming increasingly rare, and students are facing the possibility of a future made up of freelance gigs and short-term contracts. It’s no wonder they feel compelled to create complex online identities. In an economic moment in which their online identities can determine their ability to earn a wage, it’s incumbent upon them to create charismatic online personae.
Anyone else have a weakness for those “What’s in your bag?” features? My stuff is not nearly as nice as the stuff those people carry, but deep in my heart, I seem to cling to the belief that my life really would be better if I could just optimize a few things.
Anyway, I posted on Facebook about a new receipt-filing thing I’d bought, and the response was so enthusiastic (what is wrong with my friends?) that I thought I’d do a quick post about what I’ve been carrying lately. I’ve been traveling for work a ton this year (way too much, obviously) and I’ve been devoting more thought than I’d like to admit to making my conference travel bag efficient.
Motion pictures’ utility for surgeons might seem to be their ability to show things just as they appear to an observer present at the scene. But a film like Sarnoff’s suggests that there is a gulf between what mechanical reproduction shows and the way that something like circulation actually appears to the surgeon present.
For surgeons like Sarnoff, the value of film wasn’t only, or even chiefly, its ability to mechanically reproduce reality, but its ability to function as a dynamic college: to offer students of surgery a lesson on how to move back and forth seamlessly between the messy substance of reality and the neat diagrams that populate anatomical atlases.
I was especially happy to write something for the NLM because the Library’s History of Medicine division has been invaluable to my work. From my first, exploratory research into my dissertation, their librarians and archivists have been true research partners (and sometimes cheerleaders!). The History of Medicine division does invaluable work, and I’m so grateful to its staff.
This is a lightly edited version of the keynote address I was honored to give at the Keystone Digital Humanities Conference at the University of Pennsylvania on July 22, 2015. Thank you to the organizing committee for inviting me!
My sincere thanks, too, to Lauren Klein and Roderic Crooks for their advice and feedback on this talk. I’d also like to acknowledge the huge intellectual debt I owe to David Kim and Johanna Drucker, with whom I’ve argued, negotiated, and formulated a lot of these ideas, mostly in the context of teaching together. David’s important dissertation, Archives, Models, and Methods for Critical Approaches to Identities: Representing Race and Ethnicity in the Digital Humanities (UCLA, 2015), takes on many of these issues at much greater length.
I gave the title of this talk to Dot Porter some time ago in a fit of ambition, and it’s seemed wildly hubristic to me ever since. But it’s something I care a lot about, and so tonight I’d like to outline some ideas about how digital humanities might critically investigate structures of power, like race and gender.
We are doing some of that now, as evidenced by some of the work at this conference, but I don’t think we’re doing it with the energy or the creativity that we might. I’ll argue that to truly engage in this kind of work would be so much more difficult and fascinating than we’re currently talking about for the future of DH; in fact, it would require dismantling and rebuilding much of the organizing logic, like the data models or databases, that underlies most our work.
So I’ll start by saying a little about where I think we are with digital humanities now, and also about some new directions, with respect to these structures of power, that I’d like to see the field go.
As I’ve often mentioned, I’ve been working for quite some time on a study of the photographs of Walter Freeman. Freeman, a Washington, D.C., based physician, was the world’s foremost lobotomist; it’s estimated that he lobotomized some 3,500 people.
He was also a prolific and dedicated photographer. He almost invariably took photos of his patients before and after the procedure, acquiring reams of these images over the course of his career. In a chapter of my book, Depth Perception, I argue that Freeman was participating in a much longer-standing tradition of psychiatric photography, one that claimed that the human face could reveal the depths of the soul. (You can see a recorded version of the story of Freeman’s photographs here.) (more…)
This is a talk that I gave at the Harvard Purdue Data Management Symposium on June 17, 2015, in Cambridge, Massachusetts. The audience was mostly librarians and other data-management professionals. I was the only humanities person on the program, so I wanted to talk about the ways that humanists think about data differently from people in some other fields.
Today I’d like to talk about the ways in which humanists think about data, and how that’s distinct from the ways in which scientists and social scientists think about it.
Even though I think our issues can be pretty different, I want to make the case that there are some very promising ways in which libraries could make meaningful interventions in the humanities research lifecycle, both for what we might call traditional humanists and for digital humanists. So I’ll start with what “traditional” humanists might need help with and then move on to the needs of what we call “digital humanists” (although I think in practice the distinction is a bit blurred).
I just want to say at the outset that there are people who specialize in humanities data curation, and I am not one of those people. A number of talented people, including Trevor Muñoz at the University of Maryland and Katie Rawson at the University of Pennsylvania, have started to take a very programmatic look at the data-curation needs of digital humanists. And I encourage you to check out their important work. But you don’t have Trevor or Katie; you have me! So what I can do is share my own perspective and experience on what it means to work with data as a humanist, and where libraries can help.
I’ve used Indiana University’s Cushman Collection of photographs before, in my Palladio tutorial. Google Fusion tables, though, is a slightly simpler way for people to get started with data visualization. So here’s a quick tutorial that uses the same data to create a map and some simple charts.
You can also download this tutorial as a PDF or a Word document (in case you’d like to modify it).
As a teacher, I’ve always operated on the assumption that students are primarily interested in each other. Here’s a fun activity that takes advantage of that interest to teach students a little about data visualization. It’s an extremely unscientific Cosmo-style quiz, designed to show students which interests they have in common with each other. It’s just an introductory lesson, but it gives you a fun dataset to play with. You’ll probably want to split this among a few class sessions, since students will need at least one full class to just get familiar with Gephi.
Of course, it’s also a good chance to talk about how authoritative graphs like these can look, and whether the data these contain actually means much at all. (Probably not!)
Make a questionnaire for your students
I’d do this about a week before you do the dataviz lesson. I used Google Forms for this. Just to make things more fun, I called it the Mysterious DH Questionnaire. I asked five questions, each of which had five options. The possible answers were literally the first options that occurred to me.
Of course, you can choose whatever you want; just be sure you have a constrained list of choices (no write-ins).
Make your spreadsheet into a two-mode edge list
Now that you have your data, you want it in three different formats: 1) raw; 2) an edge list for a two-mode network graph; and 3) an edge list for a one-mode network graph. To get your two-mode list, use Open Refine to transpose columns across rows. The idea is to go from the layout shown in the above screenshot to …
… this one. It’s the same data, just rearranged into two columns.
Make your spreadsheet into a one-mode edge list
Then, if you want (you don’t have to, but it can help students see the difference between one-mode and two-node network graphs), you can project your two-mode edge list into a one-mode edge list, using Gephi and this tutorial from Shawn Graham.
Make an alluvial diagram
You can do this with the class. Use RAW to make alluvial diagrams from the raw dataset, experimenting with different categories. It’s fun to see the various relationships between, say, book and movie preferences.
Make network graphs
When the class is ready, move on to using the datasets to show which students have the most in common. Here’s a tutorial I prepared for students to use with this dataset (names have been blurred out). (And here’s a Word version of the Gephi tutorial, in case you’d like to alter it.)
Start with the two-mode network diagram, and when the class is ready, move on to the one-mode. Students really enjoyed seeing who had the most in common, examining the communities Gephi was able to detect, and comparing those communities to their own groups of friends.