Introduction by OpenMethods Editor (Aurélien Berra): This article reflects on the lessons learnt by the author as he first taught a graduate course in digital analysis of literary texts. He stresses the importance of methodologies over technologies, the need for well-curated, community-created teaching datasets and the implications of the practical, discipline-based organisation of the curricula.
When I set out to teach a graduate English course titled “Literary Data: Some Approaches” in the spring of 2015, I was on a mission. I wanted my eleven Ph.D. students to learn not simply how to talk about DH but how to analyze data as part of their literary scholarship, to be able not only to argue about “data” but to argue with data. I wanted to prove that English graduate students could do more than play with computers in their first DH course. At the same time, I wanted students to acquire the conceptual sophistication that would make their practical knowledge meaningful. Though my students made remarkable practical and conceptual progress, at the end of the semester my highflown aims still seemed to lie beyond our immediate grasp. Having made the attempt, however, I learned some lessons of my own about what is needed in order to take on this pedagogical mission: not only lessons in course-planning but lessons about what the scholarly community has to do – and what institutions must be prepared to supply – if quantitative methods are to fulfill their promise for the study of literature and culture.
Original publication date: 2018.