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Digital Humanities and Mary Shelley’s The Last Man

An Introductory Teaching Unit on Digital Humanities Analysis and the Nineteenth-Century Novel

Introduction

Part 2 of 3. Read Part 1: Dealing with Humanities Data.

After failing to interest CSS students in their big data questions, the English students became determined to do their own DH projects, albeit with out-of-the-box DH tools. I identified tools and provided prompts to help them analyze Mary Shelley’s novel, The Last Man (1826), using methods that were new to them.

 

Note: Content adapted from original curricular project

Overview

Cornell’s Academic Technology Specialist, Brooke Bergantzel, and I began class introducing the basic functionality of each tool and then following up with each group about their specific platform. Each group was assigned preliminary readings and expected to do additional research as needed and work outside of class (with support from Brook and her staff). They presented their final projects at the end of the term, along with reflection on their learning.

This was the second of three units that students completed during the course and took the place of a more traditional paper or mid-term assignment. Students formed four groups of three and four students. Each group was assigned a unique prompt and relied on a different DH tool:

TWINE

Group 1 used TWINE to program used to create interactive, branching stories online, to reconstruct The Last Man from the first-person perspective of a secondary character.

Voyant

Group 2 used Voyant, a suite of data analysis and visualization tools, to learn about each of the novel’s central characters and their relationship to nature.

Group 3 used a free family tree software to visually represent the complex relationships between the fictional characters and the historical/autobiographical figures upon whom they were based.

ArcGIS

Group 4 used ArcGIS to create an interactive map that represents global movements of character groupings (and individual characters who travel alone), war, and the infectious pandemic; what geographical patterns do you notice?


Goals

Updated Jul 12, 2018

Learning Objectives

Students will

  • Engage deeply with Shelley’s novel in new ways
  • Gain experience with, and confidence using, DH methods and tools for literary analysis
  • Develop writing, reasoning and oral presentation skills
  • Develop skills in collaborative learning, team-based project design, and creative risk-taking
  • Gain experience using “proto-typing” and “user-data” to improve products.

Dissemination Strategies

Teaching Notes and Assessment

Student Feedback

  • Assignment was fun to work on
  • Encouraged reading closely while being more interesting than a traditional close reading paper
  • Enjoyed working in groups
  • Each group had important contributions to the overall learning
  • Assignment provided useful, new skills

Outcomes with respect to individual learning objectives varied, primarily due to the four platforms elicited varying encounters with technology:

Engagement with and analysis of the novel

Some technologies, like ArcGis and Voyant, require more detailed textual analysis than others. For both projects, students had to pay close attention to data categories and Shelley’s language. The family tree and Twine projects required analysis of characters and plot, but less attention to the specifics of Shelley’s language. A future iteration of the assignment might want to account for these differences.

Experience with and confidence in using the technology

The mapping group had a particularly steep learning curve because none of the students had previously used spreadsheets with GSI. Much of their work had to be redone due to initially disregarding instructions to develop and use a controlled vocabulary across group members inputting the data. They weren’t prepared to decide how to locate place-names from the novel that no longer exist as such. Unfortunately, they did not consider in advance a way to code place-names from the novel differently from those provided by the software, so the spreadsheet and map include only current place-names.

The least successful project in this regard was the family tree project because we could not identify appropriate free software. The students ultimately used Draw.io, but they lacked the know-how to make the trees interactive.

Collaborative learning, teamwork, and risk-taking

This class was composed of upper-class students with a lot of experience working in teams, and they had something to prove (to DS students). Students enjoyed the groups they had been assigned to and were invested in producing something that could be built upon by future classes.

To develop reading, writing, and presentation skills

The first feedback session was structured as a timed poster presentation, the second as a Q&A, and the third as a more formal presentation of their product and their reflections. Some groups were more successful than others dividing the work appropriately.

 

In the future, I would provide a more detailed rubric for the final product (including notes on design, functionality and writing) and more guidance on how to successfully write collaboratively. It also took some coaching to help them consider how the quantitative findings might also be qualitatively important and focus their analysis there.


Collaborating partner(s)
Michelle Mouton
Professor, Cornell College
English
mmouton@cornellcollege.edu
Ross Sowell
Associate Professor, Cornell College
Computer Science
rsowell@cornellcollege.edu
ACM Program Funding
SAIL
Award
-
Funding Cycle
2016-2017
Project Duration
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