Assistant Professor of Digital Humanities at UIUC’s School of Information Sciences

Office Hours

By appointment, using Calendly link (available on Canvas or Google Drive)

Class Meetings

Tuesday and Thursdays 1:30pm-2:50 pm CST

Course Description

Currently the iSchool catalogue lists this following course description:

Explores use and application of technology to scholarly activity in the humanities, including projects that put classic texts on the web or create multimedia applications on humanities topics.

This isn’t necessarily wrong, but not quite descriptive enough for this version of the course.

Our goal in this course is to start to understand how culture broadly defined can be represented as data and studied with computation. By culture, we don’t mean bacteria cultures, but rather the type of culture that is usually associated with academic disciplines in the Humanities, such as English, History, Fine Arts, etc.. Culture in this context encompasses everything from popular fiction to newspapers, government documents, and even sociological studies of online communities like Reddit or TikTok subcultures. While culture is an intrinsic part of being human, today that culture is increasingly both digital and datafied. Representing culture whether digitally or as data might seem obvious initially; after all, most of how we all work and socialize is now often experienced through digital platforms, generating bytes and bytes of data. However, in this course we will investigate how representing our cultural heritage and past is rarely straightforward or without tradeoffs.

This course will also explore how humanities can change how we think about computing. We will explore how histories of data collection and computation can help us understand that these activities are fundamentally political, even if we often see them as ‘technical’ and therefore somehow neutral or objective. We will consider the ways that interpretation can become ‘baked’ into these technologies, and how in turn this makes it difficult to discern how these forces are shaping both scholarship and society.

While these are all big topics, we will investigate them through weekly readings and assignments, as well as a semester-long project. We will experience the process of working with culture as data - from developing an initial humanistic question to collecting and curating data to analyzing and communicating our findings. Through weekly assignments and projects, we will debate larger issues in computing in the humanities, including data ethics and privacy, sustainability and curation of digital projects, the possibilities and limitations of computational methods, how this relates to other disciplines, and how computing in the humanities is part of global conversations about data and society.

Overall Learning Objectives

  • Explore computing in the humanities as a research field. Through class discussions, readings, selected projects, and assignments, this course will provide an overview of the history, debates, and current trends in the field.
  • Experiment with computing in the humanities as a research praxis. Through learning coding, data analysis, and project management, this course will provide a foundation for how to make projects that blend culture and coding, as well as engage with some of the debates over how to maintain and evaluate this type of scholarship.

What comes after this course?

Much of what you do with this course depends on your interests, but at the very least, you will be well equipped to continue undertaking substantive and innovative research on culture using computation and data. These skills are incredibly useful whether you aim to be a data scientist, journalist, HCI or UX researcher, or simply someone who understands how technology and information shape our world. Ideally, I hope each of you continues to work on your final project and share your research long after the course ends.

Pre- and Co-Requisites

There is no required prerequisite but students should have some previous experience equivalent to a semester of programming, ideally in Python. Relevant courses include IS205 and IS107.

Interested students should contact the instructor if they have any questions.

Acknowledgements

Thanks to John R Ladd, Melanie Walsh, Anna Preus, Brandon Walsh, Meredith Martin, Sierra Eckert, Anelise Shrout, Cameron Blevins, Lincoln Mullen, Benjamin Schmidt, Lauren Klein, Miriam Posner, Alan Liu, Ted Underwood, and Ryan Cordell for sharing their syllabi - all of which have been immensely helpful and influential.

I also want to especially thank Rebecca Munson, who remains an inspiration for how I think and teach about data and who is sorely missed but will never be forgotten.