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Composition and Big Data

Title
Composition and Big Data / edited by Amanda Licastro and Benjamin Miller.
ISBN
9780822988199
9780822946748
Publication
Pittsburgh, Pa. : University of Pittsburgh Press, [2021]
Manufacture
Baltimore, Md. : Project MUSE, 2021
Copyright Notice Date
©[2021]
Physical Description
1 online resource (272 pages): illustrations
Local Notes
Access is available to the Yale community.
Notes
Description based on print version record.
Access and use
Access restricted by licensing agreement.
Summary
"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"-- Provided by publisher.
Variant and related titles
Project MUSE complete collection 2021.
Format
Books / Online
Language
English
Added to Catalog
July 17, 2023
Series
Book collections on Project MUSE.
Pittsburgh series in composition, literacy, and culture
Contents
‡t Learning to read again : introducing undergraduates to critical distant reading, machine analysis, and data in humanities writing / ‡r Trevor Hoag and Nicole Emmelhainz
‡t A corpus of first-year composition : exploring stylistic complexity in student writing / ‡r Chris Holcomb and Duncan A. Buell
‡t Expanding our repertoire : corpus analysis and the moves of synthesis / ‡r Alexis Teagarden
‡t Localizing big data : using computational methodologies to support programmatic assessment / ‡r David Reamer and Kyle McIntosh
‡t Big data as mirror : writing analytics and assessing assignment genres / ‡r Laura Aull
‡t Peer review in first-year composition and STEM courses : a large-scale corpus anaylsis of key writing terms / ‡r Chris M. Anson, Ian G. Anson, and Kendra Andrews
‡t Moving from categories to continuums : how corpus analysis tools reveal disciplinary tension in context / ‡r Kathryn Lambrecht
‡t From 1993 to 2017 : exploring "a giant cache of (disciplinary) lore" on WPA-L / ‡r Jenna Morton-Aiken
‡t Big-time disciplinarity : measuring professional consequences in candles and clocks / ‡r Kate Pantelides and Derek Mueller
‡t The boutique is open : data for writing studies / ‡r Cheryl E. Ball, Tarez Samra Graban, and Michelle Sidler
‡t Ethics, the IRBs, and big data research : toward disciplinary datasets in composition / ‡r Johanna Phelps
‡t Ethics in big data composition research : cybersecurity and algorithmic accountablitiy as best practices / ‡r Andrew Kulak
‡t Data do not speak for themselves : interpretation and model selection in unsupervised automated text analysis / ‡r Juho Paakkonen
‡t "Unsupervised learning" : reflections on a first foray into data-driven argument / ‡r Romeo Garcia
‡t Making do : working with missing and broken data / ‡r Jill Dahlman.
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