Cargando…
Composition and Big Data /
"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 citatio...
| Otros Autores: | , |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Pittsburgh, Pa. :
University of Pittsburgh Press,
[2021]
|
| Colección: | Book collections on Project MUSE.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- ‡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.