Requirements

Course Digital Infrastructure

We will make use of two different online systems in this course: this course site, and a class Box folder.

We will use our course site to manage course information and our schedule. You will find an online version of our course calendar here (including the most up-to-date version of reading assignments and due dates), as well as information about all course assignments. We will also use a shared class Box folder (miami.box.com) to distribute course readings and other materials. Every UM student, staff, and faculty member has free access to Box, but you will need to create a Box account to access the shared folder if you don’t have one already. You will receive a link to this shared folder via email.

Assignments

You must complete all assignments to pass the class.

Readings

All of our course readings are available online or through our shared Box folder. However, we will be reading three chapters from Andrew Piper’s recent book, Enumerations: Data and Literary Study (University of Chicago Press, 2018). While these chapters are included among the materials available via our Box folder, if you would like to read this book in its hard copy form, you may wish to purchase it.

Attendance and Active Participation

As with any graduate course, you are expected to do the reading for, attend, and actively participate in every class period, barring emergencies or illness. If you know you will miss a particular class period, please let me know in advance.

Case Study Presentation and Guide: 10%

Each week after week 1 features a “case study” reading, as marked in the syllabus. Early on in the course, you will select one case study on which to present (except for those weeks marked “INSTRUCTOR,” in which I will handle the case study presentation). Depending on the number of students in the course, we may need to double up some weeks, in which case you may complete this assignment with a partner (you will each submit your own guide, but you will do the presentation together).

To prepare for your presentation, you will fill out a “Case Study Guide” for that project/article. You can find this guide, which includes more instructions for completing it, in our shared Box folder. After completing the guide, you will upload it to our Box folder with the following filename structure: WeekNumber-YourName-CaseStudyFirstAuthorName. You will also add the tools and/or methods the author(s) of your case study used, and short descriptions of each, to our running “Tools and Methods” Google doc. Your guide and your contributions to the tools and method list are both due by class on the day for which you have signed up to present a case study.

Your case study presentation will simply be a presentation of your guide to the class. You should use the guide to structure your presentation, answering each question in your presentation and providing more detail as necessary. If presenting alone, your presentation should be about 8-10 minutes long (10 minutes is a hard limit). If presenting with a partner, it should be about 15-20 min (20 min is a hard limit). Your case study presentation and guide will be graded on completion.

Tutorials: 15%

Tutorial Due Dates:

Tutorial 1: Monday, Feb 18
Tutorial 2: Monday, Feb 25
Tutorial 3: Monday, March 4

We will complete 3 tutorials designed to teach the basics of coding in R. These tutorials are due over a period of 3 weeks – 1 per week – during the first part of the semester. We will also find a good time during these weeks to hold weekly coding work sessions outside of class. I will be available during this time for drop-in help with the tutorials and other challenges, and class members can help each other during this time as well.

As is emphasized in the course description, these tutorials are not designed to teach you how to code in R. That is not really possible to learn in three weeks. Rather, they are designed to introduce you to the basics of coding in R, and to give you some hands-on experience with it, so that we can better understand how researchers use R to analyze humanities data. Remember, our goal is an introductory level of literacy.

You will complete these tutorials as R Markdown notebooks, and you will submit them to our shared Box drive by class each day they are due. We will discuss all of the tutorials in class on Monday, March 4. Your tutorials will be graded on completion.

Data Set Analysis: 30%

Due:

You should choose your data set by Monday, Feb 11. Email me with your choice by class, including a citation of the article/project/repository it comes from (option 1 below), or a description of the hypothetical data set you are creating (option 2).
Analysis paper due Friday, March 8. Email me your paper and your data set, or portions of it (or links to it).

In the spirit of the “Data Sets” section in CA: Journal of Cultural Analytics, your data set analysis paper will be a reflection on and analysis of a humanities data set. Read the CFP on the “Data Sets” page to see what kinds of questions your paper should answer, but in brief: Your paper should introduce and describe your chosen data set, but the majority of the paper should address its affordances and limits, what kinds of questions it allows researchers to ask, and what other issues, questions, and/or data in its field it is in conversation with.

While this specific kind of paper may be new to you, what I am asking for here is still a research paper. This means your data set analysis should demonstrate knowledge of its field (i.e., post-1945 US literature, or what have you), and it should contribute to knowledge in this field. It should be ~2500 words long, and it should include a works cited page/bibliography.

You can go about selecting your data set in one of two ways:

Select an existing data set used in one of the course readings (required or additional), or in another article, project, or repository you have found through your own research. I encourage you to select data related to your areas of interest; the more you know, and care, about the data, the better. Ideally, the authors of the data set will have described how they collected and their data and will have made their data, or portions of it, available so that you can see it for yourself. Barring that, you should choose an article, project, or data set in which the authors describe how they have collected their data so that you can recreate this process (or aspects of it). See the “Data Sets” portion of the Additional Readings page for more possibilities.
Select a data set that, as far as you know, doesn’t yet exist but that you would like to create. For the purposes of this assignment, I don’t expect you to necessarily be able to create the full data set, as that process is usually very labor-intensive. Rather, if you choose this option, I expect you to be able to: 1) Describe the conceptual boundaries of this hypothetical data set, what exactly it includes and what it doesn’t and what this data looks like (its format, where it comes from, etc); and 2) Describe, in some detail, how you would go about collecting this data and creating this data set, if you had the time and the resources. What would this process actually entail? If you choose this option, I strongly recommend you talk with me before the Feb 11 data set selection due date.

No matter the option you choose, you should be able to access or collect at least some portion of your chosen data set yourself so that you can get a sense of what the data looks like.

Final Paper: 45%

Due:

Abstract: Monday, April 1. Email to me by class.
Annotated bibliography: Monday, April 15. Email to me by class.
Final paper: Friday, May 3. Email to me.

Final Paper
Your final paper in this class is not a work of actual computational criticism; rather, it is a work of speculative computational criticism. We will talk more about this in class, but in general, your tasks in the final paper are to: 1) Pose a research question or questions amenable to computation, making sure to describe how and why it is amenable to computation; 2) Describe how this question relates to existing concerns in the field(s) in which you are posing it, and/or has the potential to fill existing gaps in this field(s); and 3) Describe in some detail the data and methods you think you would need to collect and employ in order to answer this question, and why. You might think of the final paper, then, as something like a prospectus or a proposal for a work of computational criticism. I encourage you to use the data set you selected for your data set analysis paper in your final paper, but you don’t have to.

Your final paper should demonstrate not only some familiarity with methods in humanities data analysis, but also knowledge of the field or fields within which you see your project as situated. It should be ~2500-3000 words long, and it should include a works cited page/bibliography.

Annotated Bibliography
This is a formal bibliography of about 8-10 sources (articles, book chapters, projects) you see as potentially important to your final paper. You should include citation information for each source, as well as a 1-paragraph summary of each source and its relevance to your project. The bibliography should be done in MLA format. You may include up to 3 sources from our syllabus (the required readings); other sources should be drawn from your own research or from the additional readings list for this course.

Your bibliography should be divided into 2 categories: 1) Sources related to the topic of your final paper; 2) Sources related to the methods of your final paper. You should include an approximately equal number of sources in each category.

For samples, see https://owl.purdue.edu/owl/general_writing/common_writing_assignments/annotated_bibliographies/annotated_bibliography_samples.html.

Abstract
The abstract lays out the general gist of your ideas for your final paper. It should be no longer than 1-2 paragraphs. The more specific you can be at this stage, the better. While a good deal of thought should go into the abstract, it does not commit you to anything, and the project may change as needed as you work on it.