Research Projects
There are not many courses where you can study whatever you want, however you want. This one comes close. The centerpiece of the course is a research project on a topic driven entirely by your own interests, and you will begin applying what you learn to it almost immediately. So think creatively, and think outside the box: what have you always wanted to know about a text, a body of work, or a corner of the past, that a computer might help you see?
What Most Projects Have in Common
None of these are hard rules, but most projects share a few features.
A text, or a body of texts, free of copyright restrictions. It could be a thousand pages. It could be one page. It depends on the material and on the question. (Copyright is trickier than it looks; see Copyright before you settle on a source.)
A focus on text. Much of this course is about modeling texts: What is a text? What are its features? What can we know about it, and how can computers help us understand it better? So most projects are text-centric in one way or another. That still leaves a lot of room. An exhibition catalogue for a show of paintings is a text. So is a set of song lyrics, a collection of letters, or a run of newspaper advertisements.
Non-English texts are strongly encouraged. Not everyone on a team needs to know the language or be able to read the script. Different people can do different parts of a project, and in this course that happens all the time: some team members are drawn to reading the text itself and the scholarship about it, while others gravitate toward the coding. Both are essential.
Beyond that, there is not much to say in general terms, because what you do will depend on your own interests and your own disciplines.
Teams
Working in a team is strongly encouraged, though not required. The sweet spot is three students, perhaps four. Five gets hard to coordinate; two is small. The expectations for what a good project looks like do not change with the size of the team, so there is a built-in incentive to aim for the sweet spot.
Mentors
Project mentor. Every project team has a mentor from the instructional team: either me or one of the undergraduate TAs. All projects meet weekly with their mentor. Many recitation sessions are dedicated to these meetings, so do your very best to make sure that everyone on your team attends the same recitation (see Recitations).
Subject-matter mentor. We strongly encourage, but do not require, each team to find a faculty member who works on something close to your project’s topic. This person may know nothing about coding or AI, and that’s fine: you are going to them for high-level, field-specific advice, above all about what makes an interesting research question in their field, regardless of method. Consider someone whose class you enjoyed, or someone you have worked with before; we are also happy to suggest names. These are busy people, so expect feedback once or twice in the semester, not weekly.
How a Project Unfolds
Every project is different, but most follow the flow of the semester.
Getting started. The first few weeks are about settling on a team, a regular meeting time, a general topic, and a text.
Analyzing and modeling the document. Every document contains a nearly infinite amount of information, if you think about it. What kind of paper is it on? What genre is it? What ink was used? What is its rhyme scheme? Who is mentioned, and where? You can model almost anything, so a big part of this course is thinking carefully about what you are actually interested in, because the way you model a text is inseparable from your research question. In coding terms, this is the work of designing a schema. Expect to spend much of the first half of the semester here.
Encoding. Next comes marking up your text according to your model. Here projects begin to diverge. Some spend a lot of their time working out ways to tag and model their text automatically, with tools like regular expressions.
Processing and publishing. In the later part of the semester, projects diverge further. Many produce some kind of digital edition, so that readers can encounter the same text you find compelling, enriched with everything you have encoded in it. Others focus on graphics that summarize what you have learned about the text: anything from charts and graphs to network analysis to digital mapping.
Throughout the semester there will be check-ins on your project: sometimes a submission, sometimes a journal assignment. See the syllabus for these.
Different Ways In
Projects from earlier semesters show just how many directions a project can take. A few examples:
- Language itself: Shakespeare’s Neologisms, or Sevdah, an etymological study of Bosnian song and poetry.
- Representation and theme: Gender in the Iliad, Women in Folklore, and Motherhood and Power through Greek Mythology.
- Documents of power and persuasion: Constitutions of the Former Soviet Union, Presidential Propaganda, and Soviet Textbooks.
- A single author or work: Literary Analysis Engine for Oscar Wilde, and Perceptions of the Black Death in the Decameron.
For the full list, see Past Projects.
Your Project and Your Grade
You need to be able to explain the code behind your project: what it does, why it is there, and how you know it works. Comment it as you go, including where AI wrote it and what you checked (see AI Policies for an AI Course). At the end of the semester, your final report accounts for your project, including who on the team did what (see (Un)Grading).