Blogging Pedagogy - distant reading

Graphing Empathy

Scott Garbacz — Fri, 25 Apr 2014 11:20:32 +0000

Author:

Scott Garbacz

Image Credit:

Survey created in Canvas.

This semester, I taught a Banned Books class focusing on the ways that authors deploy empathy. One cornerstone of the class was a series of daily surveys. Each discussion was preceded by a survey (pictured above) in which students gave an informal ranking of their empathetic response to the main character(s) featured in the day’s readings. My goal was to help students theorize their own responses to stories, but I also ended up generating some unexpected revelations.

The surveys about The Satanic Verses went as I expected, but not as my students expected, which provided a valuable learning opportunity. Initially, the novel’s two Indian protagonists were too alien for students to initially identify with them. As a result, student empathy with both characters increased as they found themselves capable of projecting their own identities into Saladin Chamcha and Gibreel Farishta. Halfway through the book, however, they were surprised to find their ability to empathize with the characters to be dramatically undercut. My general conclusion is that this dropoff was caused when the details of Rushdie’s novel began to violate students’ expectations of the character. The students own responses, however subjective or imprecise, allowed me to introduce the concept of “false empathy,” where a deep sense of empathic connection actually serves to blind students to the realities of these characters. The temporary drop in students’ empathy, then, might actually reflect better reading practices, as they deconstruct false images of the character and began to grapple with the unfamiliarity of the characters. This in turn lead to better, perhaps less false, empathy. Most students ended up reporting their strongest connections with the characters as they concluded the novel.

The graph for Toni Morrison’s The Bluest Eye, unfortunately, was less enlightening—probably because I chose the wrong character. I had my students rate their connection not with the traumatized Pecola, but with the book’s primary narrator, Claudia. Since her character was not particularly dynamic, students quickly built up empathy for her and stayed quite empathetic. In fact, the only point of interest was a related poll I did on students reactions to the aged child molester who appears later in the book; those results, however, are beyond the scope of this discussion.

Huckleberry Finn generated the most interesting results, managing to truly surprise me. This was true not in the overall graph, but in the details. Generally, Huck followed a similar sine-curve pattern to Gibreel and Saladin, while the less complex (and more passive) escaped slave Jim consistently gained empathy in a linear pattern close to that of Claudia. Yet on the day that students read about Huck’s climactic decision to free Jim even if it meant embracing a life of wickedness, something interesting happened.

Above, you can see a more detailed breakdown of student’s responses before and after they read Chapter 31. Before, most students felt a sense of “moderate emotional connection” with Huck. That is, they felt emotionally tied to Huck Finn’s fate, but they didn’t identify with him on a deep level. After Chapter 31, the class polarized. A narrow majority, as I suspected, responded to Huck’s troubled theological and ethical musings by doubling down on their emotional investment in the character, reporting a newfound “strong emotional character.” But a very significant portion of the classroom found the chapter distancing. They reported being able to “see where he [Huck Finn] was coming from” intellectually, but they lost (at least temporarily) their ability to empathize with Huck.

Charting empathy using an online survey at the beginning of class turned out to be not only a great teaching opportunity, but a great learning opportunity. It certainly didn’t provide rigorous data, and I would be hesitant to make any firm claims based on such an informal series of surveys, but it did provide something valuable: a new way of thinking about how students read, and a series of talking points allowing students to reconsider the nature of their empathic connections with fictional characters.

Automated Textual Analysis in Revision

Mike Widner — Tue, 30 Jul 2013 05:00:00 +0000

Author:

Mike Widner

Image Credit:

Screen shot of Voyeur

I like to discover and play with digital humanities tools. One I recently discovered is Voyeur, which creates word clouds and word frequency graphs for texts you provide it. Despite the warning by Jacob Harris that we should be wary of word clouds, they can serve as a gentle introduction to automated text analysis for students. While experimenting with Voyeur, I wondered how I could best use this tool to expose my students to the ways the digital humanities are transforming how we interact with and study literature. Rather than explore a literary text, however, I decided that it might be interesting to see if textual analysis can help with the process of revision. My hope is that this exercise might make students see the value of such tools in a different way and see their own writing as texts available to (and requiring) interrogation.

Voyeur is an easy tool to use. You simply upload or paste a text and click “Reveal”. You’re then provided with a workbench-like screen that starts with a word cloud and copy of the text you submitted. You can also upload multiple documents in many common file formats such as Microsoft Word or PDF. Doing so allows Voyeur to create a corpus of texts that it can then compare for word frequencies. Voyeur also provides two pre-defined corpora: one of Shakespeare’s plays (which would be fun for a course on his works) and another of a humanist listserv archive. The initial word cloud includes every word in the document, which is often not very useful because of common words like “the,” but the tool provides a pre-programmed list of “stop words” that will cause Voyeur to redraw the word cloud with those words omitted. Clicking on any word in the cloud will provide a graph of the relative frequency of that word. The tool also provides information about vocabulary density, distinctive words, length of documents, and a number of other statistical details worth investigating.

Rather than delve into the possibilities some of the more advanced statistical measures provided, I decided to focus on word clouds and word frequencies when I use this tool with students; those two elements seem the most accessible to students who probably lack experience with automated textual analysis. I also want to focus on seeing if this approach can help address one of the biggest and most prevalent problems I encounter in student writing: the difficulty stating a clear thesis and then staying focused on that thesis throughout the paper. I wondered, then, if a word cloud and graphs of word frequencies might allow a way to visualize the actual (rather than implicit or imagined) topics of a paper and their appearance and disappearance in different sections. I came up with this exercise as a result of these ideas. Note: I teach in a classroom that provides a computer to each student. Still, this exercise could, with only a little effort, be repeated with laptops or outside of class time. Voyeur does allow users to export all data, so it would be fairly simple to share work.

First, have students upload their papers to Voyeur. If they have multiple revisions of a paper, all the better, as it allows a comparison of the iterations of their writing. After setting the stop words and exploring the different word frequencies of their own work, students should then trade and look at a peer’s work. This switch allows the students to avoid being biased by what they think the paper is about and instead focus on what Voyeur shows. Here are a few instructions I have come up with for students to ask:

Use Voyeur to see if you can get an idea of the paper’s thesis and how the argument progresses without reading the paper.

What can you determine about the paper's organization?
What words are most common? What words would you expect to be most common based on the thesis?
What words rise and fall (or do not) in frequency together? Would you expect them to do so?
How can you revise your paper so that the most important words to the argument appear more frequently or in more effective combinations?

I have tested this technique on some of my own writing and found that it does, in fact, reveal some interesting patterns. For example, in one essay I wrote, I juggle three major topics, which rise and fall in sequence through the paper. It was surprising to me to see just how regularly the paper followed that pattern, in fact, and confirmed that I had organized it in a logical manner that accords with my argument. What other uses might you suggest for Voyeur? Are there other questions you think I could pose for students as they use this tool to analyze their own writing? Do you know of other tools that might be useful for this exercise?