Course Description

MW 12:00-1:20pm | 1131A JFSB

If you Google “undergraduate research,” you get one thing, over and over again: students in lab coats. It’s an accepted part of training for students in the sciences to work as part of a team to learn something new. The students might contribute only in small ways and they work under the direction of a faculty member, but it doesn’t change the fact that they are investigating something that the professor does not yet know.

What does undergraduate research in the humanities look like? Rather than hours in a lab, it involves hours in a library. Rather than working in a team, it generally means working on your own. And, in my experience, it tends to mean reading the work of other scholars—which the professor almost certainly knows already—on a subject and then synthesizing them to inform your interpretations of one text or another. There’s absolutely nothing wrong with this form of research; indeed, it prepares you for any number of different things one does in the “real” world.

While situated firmly in the humanities, this course aspires to something different. We will work together to investigate something that I do not yet know: the relationship between the first five books of poetry by Carol Ann Duffy. We will do this using digital humanities methods, seeking to answer a question that other scholars have not yet considered. In the end, we will collectively present what we have learned to the world.

By the end of the course, we will all have more experience in humanities research, digital humanities methods, and working collaboratively.

Course Goals

At the end of the course, students will have

  • demonstrated an advanced level of knowledge in a chosen specialty area and conduct research in that area
  • collaborated on research in a field that has traditionally privileged individual scholarship
  • improved their writing by writing publicly
  • learned specific technologies for digital humanities research, including the command line, GitHub, Zotero, XML encoding according to TEI standards, and some Python
  • practiced deploying and the interpreting the results of computational text analysis