Composition and Big Data
Published on 2. November 2021
329 pages
978-0-8229-8819-9 (ISBN)
Description
In a data-driven world, anything can be data. As the techniques and scale of data analysis advance, the need for a response from rhetoric and composition grows ever more pronounced. It is increasingly possible to examine thousands of documents and peer-review comments, labor-hours, and citation networks in composition courses and beyond. Composition and Big Data brings together a range of scholars, teachers, and administrators already working with big-data methods and datasets to kickstart a collective reckoning with the role that algorithmic and computational approaches can, or should, play in research and teaching in the field. Their work takes place in various contexts, including programmatic assessment, first-year pedagogy, stylistics, and learning transfer across the curriculum. From ethical reflections to database design, from corpus linguistics to quantitative autoethnography, these chapters implement and interpret the drive toward data in diverse ways.
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Amanda Licastro | Benjamin M. Miller
Composition and Big Data
Book
12/2021
University of Pittsburgh Press
€58.00
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Persons
Amanda Licastro (Editor)
Amanda Licastro is assistant professor of digital rhetoric at Stevenson University in Maryland. Her research explores the intersection of technology and writing, including book history, dystopian literature, and digital humanities.
Benjamin M. Miller (Editor)
Benjamin Miller is assistant professor of composition in the English Department at the University of Pittsburgh, focusing on digital research and pedagogy. He is the author of the poetry collection Without Compass.
Amanda Licastro is assistant professor of digital rhetoric at Stevenson University in Maryland. Her research explores the intersection of technology and writing, including book history, dystopian literature, and digital humanities.
Benjamin M. Miller (Editor)
Benjamin Miller is assistant professor of composition in the English Department at the University of Pittsburgh, focusing on digital research and pedagogy. He is the author of the poetry collection Without Compass.
Content
- Intro
- Contents
- Acknowledgments
- Introduction: Reasons to Engage Composition through Big Data | Benjamin Miller and Amanda Licastro
- Section One: Data in Students' Hands
- 1. Learning to Read Again: Introducing Undergraduates to Critical Distant Reading, Machine Analysis, and Data in Humanities Writing | Trevor Hoag and Nicole Emmelhainz
- 2. A Corpus of First-Year Composition: Exploring Stylistic Complexity in Student Writing | Chris Holcomb and Duncan A. Buell
- 3. Expanding Our Repertoire: Corpus Analysis and the Moves of Synthesis | Alexis Teagarden
- Section Two: Data Across Contexts
- 4. Localizing Big Data: Using Computational Methodologies to Support Programmatic Assessment | David Reamer and Kyle McIntosh
- 5. Big Data as Mirror: Writing Analytics and Assessing Assignment Genres | Laura Aull
- 6. Peer Review in First-Year Composition and STEM Courses: A Large-Scale Corpus Analysis of Key Writing Terms | Chris M. Anson, Ian G. Anson, and Kendra Andrews
- 7. Moving from Categories to Continuums: How Corpus Analysis Tools Reveal Disciplinary Tension in Context | Kathryn Lambrecht
- Section Three: Data and the Discipline
- 8. From 1993 to 2017: Exploring "A Giant Cache of (Disciplinary) Lore" on WPA-L | Chen Chen
- 9. Composing the Archives with Big Data: A Case Study in Building a Collaboratively Authored Metadata Information Infrastructure | Jenna Morton-Aiken
- 10. Big-Time Disciplinarity: Measuring Professional Consequences in Candles and Clocks | Kate Pantelides and Derek Mueller
- 11. The Boutique Is Open: Data for Writing Studies | Cheryl E. Ball, Tarez Samra Graban, and Michelle Sidler
- Section Four: Dealing with Data's Complications
- 12. Ethics, the IRBs, and Big Data Research: Toward Disciplinary Datasets in Composition | Johanna Phelps
- 13. Ethics in Big Data Composition Research: Cybersecurity and Algorithmic Accountability as Best Practices | Andrew Kulak
- 14. Data Do Not Speak for Themselves: Interpretation and Model Selection in Unsupervised Automated Text Analysis | Juho Paakkonen
- 15. "Unsupervised Learning": Reflections on a First Foray into Data-Driven Argument | Romeo Garcia
- 16. Making Do: Working with Missing and Broken Data | Jill Dahlman
- Contributors
- Index
