Composition and Big Data

by Amanda Licastro (Editor), Benjamin Miller (Editor)

Overview

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.

Product Details

ISBN-13:	9780822988199
Publisher:	University of Pittsburgh Press
Publication date:	11/02/2021
Series:	Composition, Literacy, and Culture
Sold by:	Barnes & Noble
Format:	eBook
Pages:	272
File size:	4 MB

About the Author

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.

Table of Contents

Contents Acknowledgments Introduction: Reasons to Engage Composition through Big Data | Benjamin Miller and Amanda Licastro 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 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 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 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

Editorial Reviews

This is a fantastic collection of approaches to using data analytics in writing studies. I’m particularly impressed with the breadth of applications, from using big data in the classroom to tracing the contours of the discipline. For anyone concerned about the continual encroachment of data analysis and algorithmic structures on our lives as teachers and scholars, this collection offers a foundation for understanding how to turn these tools to productive uses for our own practices, which helps us to resist and mediate big-data analyses that are antithetical to the mission of the humanities.

Doug Eyman

Licastro, Miller, and their excellent cohort of contributors turn the field’s attention to the digital life of writing for a much needed ‘collective reckoning’ with the programs and systems that we use. In this timely collection, we learn that we cannot ignore how our writing is coupled to platforms that store, deliver, quantify, leverage, and profit from our everyday work.

Jim Ridolfo

Licastro, Miller, and their excellent cohort of contributors turn the field’s attention to the digital life of writing for a much needed ‘collective reckoning’ with the programs and systems that we use. In this timely collection, we learn that we cannot ignore how our writing is coupled to platforms that store, deliver, quantify, leverage, and profit from our everyday work.” —Jim Ridolfo, University of Kentucky

“This is a fantastic collection of approaches to using data analytics in writing studies. I’m particularly impressed with the breadth of applications, from using big data in the classroom to tracing the contours of the discipline. For anyone concerned about the continual encroachment of data analysis and algorithmic structures on our lives as teachers and scholars, this collection offers a foundation for understanding how to turn these tools to productive uses for our own practices, which helps us to resist and mediate big-data analyses that are antithetical to the mission of the humanities.” —Doug Eyman, George Mason University

From the Publisher