Author: Jon Heggestad, Lecturer, University of Wisconsin-Eau Claire
Role: Course Instructor
What did you want students to be able to do by completing this assignment?
Analyze contemporary narratives around Big Data, engaging with these critiques while exploring alternative trajectories that represent more local and personal modes of knowledge production.
Design a dataset—choosing a topic, collecting data, and cleaning it before presenting it to others.
Create a data visualization, evaluating various forms of graphical display and data visualization software through hands-on engagement.
Evaluate data collection and data visualization methods as a mode of personal reflection.
Was there anything this assignment taught students that you felt they wouldn't have been able to learn through other types of class assignments?
The hands-on aspects of this assignment encourage students to slow down, following Maggie Berg and Barbara K. Seeber’s approach to learning in The Slow Professor (University of Toronto Press, 2016). As students pause to consider their own engagements with contemporary narratives surrounding data—in terms of data collection, data visualization, Big Data, etc.—they take part in the deconstructive frameworks suggested in our class readings. This DIY component is further emphasized through the two-part design of the assignment, which allows students to test out a wide range of data visualization methods and platforms.
What is the course title and level?
EGL 311: Literary or Critical History: How Humanities Became Digital, Intermediate.
As an upper-level elective in the English department, this course catered primarily to English majors. Although I’ve taught the course several times, it most recently ran as an entirely online offering, through both synchronous and asynchronous components. The examples below come from this most recent iteration.
What kinds of prior knowledge is necessary to complete this assignment? How do students gain this knowledge?
Students coming into this course generally have very little experience with either collecting or visualizing data. Many students, I’ve found, have analyzed and critiqued narratives surrounding Big Data through a 100-level course on reading social media, also offered through the English department. Whether or not students have this prior discursive knowledge, Part I of the Humanistic Data Activity is scaffolded through scholarly texts and creative works surrounding data collection, namely through Ben Tarnoff’s “The Data is Ours!,” Jonathan Harris’s “Data Will Help Us” and excerpts from Giorgia Lupi and Stefanie Posavec’s Dear Data collaboration. These works both introduce students to more prominent narratives in the humanities that warn of the dangers posed by Capitalist-driven and surveillance-focused data collection and propose more thoughtful, local, and personally meaningful alternatives for what these narratives could be.
Part II is similarly structured through multiple readings. Johanna Drucker’s “Humanities Approaches to Graphical Display” introduces complexity and nuance to the categorization of data and its subsequent display. Along with this more theoretical work, excerpts from Alberto Cairo’s The Functional Art: An Introduction to Information Graphics and Visualization instructs beginners as to how they might go about visualizing the data that they’re working with, providing a wide array of case studies that allow students to consider how they might go about framing their own work.
Small group discussions in between these assignments provide an opportunity for students to self-correct the structure of their own datasets while continuing to sit with the larger narratives surrounding data in the humanities.
This specific iteration of the course offered students a number of entry points into the field of digital humanities, focusing on a trajectory that followed data from its inception to its visualization and evaluation. The course was framed both as an introduction to a wide array of DH methods and as a workshop for engaging with these methods, creating a portfolio that exhibited their successful implementation. Accordingly, this assignment, which spanned three weeks of the 15-week course, built on theoretical discussions surrounding data visualization, asking students to engage with data in new and personal ways.
A great deal of autonomy was granted in the execution of this assignment. In class, students were shown a number of data visualization platforms, including Excel, Google Sheets, LucidChart, and Voyant. Students with interests in these specific platforms either experimented with them on their own time or met with me during office hours for crash-course tutorials. A number of students opted for a more manual mode of visualizing their data, choosing to display it through drawings and collage. In course reflections, these students noted that their preference for analog methods was connected to their own sense of fatigue surrounding digital platforms and online classes in the midst of the COVID-19 pandemic.
As noted above, this project spanned three weeks of the course. In the first week, students were shown several examples of humanistic data projects created by the information designers Lupi and Posavec before reviewing the prompt for the assignment. Student work from past iterations of the course was made available through an online folder for those seeking additional examples. Selecting topics of their own, students then collected data over the following week, recording habits and information in notebooks and note-taking apps. This data was then cleaned to varying degrees based on the topic that each student had chosen. (One student, for example, recorded how many characters she used in her text message conversations. Wrestling with how to account for her use of emojis, she ultimately opted to regard each emoji as one character. Another student who had kept track of how often her lips felt chapped decided to simplify the time period of her data, trimming out the outlier hours of two nights that she’d stayed up later than usual, commenting that her data collecting had become more lax at these times anyway.)
During the second week of the assignment, students turned in their collected data along with a reflection of their data cleaning process and a proposal for how they planned to visualize this work. Splitting into small groups of 5-6, the class then addressed questions designed to frame these projects within the larger scope of the course:
Describe the type of data visualization that you plan to create. What would this visualization “do”? What would it convey?
In what ways does this visualization “count” as DH? How does it enhance or add to our understanding of humanistic data?
Upon receiving feedback from both their peers and their instructor, students then set to work on implementing their plans, creating data visualizations out of their datasets. Regardless of their chosen topics, platforms, and mediums, students came away with a more complex understanding of where data came from, the manual decisions around categorizing and cleaning it, and the benefits and challenges connected to its visual display.
How much time did you allot to this project?
This two-part assignment occurred over the course of three weeks. One day of class time was set aside for introducing the assignment and narratives surrounding Big Data. A week later, following the due date of the first part of the assignment, another class was set aside for students to discuss their work so far and to frame data visualizations through the other readings mentioned above. The second part of the assignment was due the following week and contained a reflection section as well. Part I required students to collect data for a week from anywhere between 5-15 minutes a day, and Part II took students an average of 3 hours to complete.
What kinds of support or training did you provide to help students learn to use new techniques or specialized tools?
Two days of instruction were set aside for scaffolding the project and providing examples that students might use as models for their own work. An overview of platforms and design mediums was also addressed at this time, and students with questions regarding specific platforms (namely Google Sheets and Voyant) met with me during office hours. I also provided links to resources and materials on Excel, Tableau Public, and LucidChart. Several students took advantage of these resources, and others discovered platforms that I was unfamiliar with.
Did you need any specialized equipment, tools, or human resources to make this assignment feasible? If so, please describe.
Cairo, Alberto. The Functional Art: An Introduction to Information Graphics and Visualization. Pearson, 2013.
Drucker, Johanna. “Humanities Approaches to Graphical Display.” Digital Humanities Quarterly, vol. 5, no. 1, 2011, http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html. Accessed 25 September 2021.
Harris, Jonathan. “Data Will Help Us.” http://www.datawillhelp.us/. Accessed 25 September 2021.
Lupi, Giorgia and Stefanie Posavec. Dear Data. Princeton Architectural Press, 2016.
Tarnoff, Ben. “The Data is Ours!” Logic, 1 April 2018, https://logicmag.io/scale/the-data-is-ours/. Accessed 25 September 2021.
How did you assess or grade this project?
For the first part of this assignment, I grade for completion while providing guidance for how datasets might need to be adjusted or cleaned. For Part II, I use a single-point rubric for this assignment that includes criteria such as content (does it display a week’s worth of data?), accessibility, appearance, and connection to course readings.
If you assigned this project again, would you change anything? If so, what?
I have used this assignment, or a version of it, in a number of my classes. I find that it’s important to offer students a wide range of data visualization projects so that they see a diverse range of what their own projects might look like without trying to adhere to any one style, method, or platform. While the current organization is a result of my own learning experience in assigning this project, I anticipate introducing students to example projects earlier in the semester the next time I assign it. The two-part project can also easily be compacted into a one-off assignment.
Describe any trouble spots or complications someone else might want to be aware of before trying a similar assignment in a course of their own.
Free and open-source platforms like Voyant are wonderful resources, but do note that they tend to be somewhat less reliable than commercial software. Warning students of this at the outset of the project (and reminding them that they may need to refresh the page or revisit the site at a later time) can prevent a good deal of panicked emails.