Composition and Big Data

Name: Composition and Big Data
Brand: The American University in Cairo Press
Price: 58.49 EUR
Availability: OnlineOnly

Amanda Licastro Benjamin M. Miller(Herausgeber*in)

The American University in Cairo Press

Erschienen am 2. November 2021

329 Seiten

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Inhalt

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

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