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 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 typically 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 IRL.
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 completely: how long story arcs last in Charles Schulz’s daily comic strip, Peanuts. We will investigate this, as well as other questions that we collectively identify, using digital humanities methods, seeking to answer questions that other scholars have not yet considered. By the end of our course, we will collectively begin authoring a report on what we have learned. What’s more, we will all have more experience in humanities research, digital humanities methods, and working collaboratively.
Course Goals
- To strengthen your testimony of the gospel
- To make it possible for you to demonstrate an advanced level of knowledge in a chosen area (chosen by me, alas) and to conduct original research in that area
- To collaborate on research in a field that has traditionally privileged individual scholarship
- To improve your long-form writing
- To learn specific technologies for digital humanities research, including the command line, version control via GitHub, XML encoding according to TEI/CBML standards, the MALLET package, and possibly the Stylo and wordVectors packages for R
- To practice interpreting the results of computational text analysis
