Composition and big data /

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"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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Licastro, Amanda (Editor ), Miller, Benjamin (Poet) (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Pittsburgh, PA : University of Pittsburgh Press, [2021]
Colección:Pittsburgh series in composition, literacy, and culture.
Temas:
English language > Rhetoric > Computer-assisted instruction.
English language > Rhetoric > Study and teaching.
Online data processing > Authorship > Study and teaching.
Big data.
Computational linguistics.
Digital humanities.
Anglais (Langue) > Rhétorique > Enseignement assisté par ordinateur.
Données volumineuses.
Linguistique informatique.
Sciences humaines numériques.
computational linguistics.
digital humanities.
LANGUAGE ARTS & DISCIPLINES > General.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • ! Learning to read again : introducing undergraduates to critical distant reading, machine analysis, and data in humanities writing /! Trevor Hoag and Nicole Emmelhainz
  • ! A corpus of first-year composition : exploring stylistic complexity in student writing /! Chris Holcomb and Duncan A. Buell
  • ! Expanding our repertoire : corpus analysis and the moves of synthesis /! Alexis Teagarden
  • ! Localizing big data : using computational methodologies to support programmatic assessment /! David Reamer and Kyle McIntosh
  • ! Big data as mirror : writing analytics and assessing assignment genres /! Laura Aull
  • ! Peer review in first-year composition and STEM courses : a large-scale corpus anaylsis of key writing terms /! Chris M. Anson, Ian G. Anson, and Kendra Andrews
  • ! Moving from categories to continuums : how corpus analysis tools reveal disciplinary tension in context /! Kathryn Lambrecht
  • ! From 1993 to 2017 : exploring "a giant cache of (disciplinary) lore" on WPA-L /! Jenna Morton-Aiken
  • ! Big-time disciplinarity : measuring professional consequences in candles and clocks /! Kate Pantelides and Derek Mueller
  • ! The boutique is open : data for writing studies /! Cheryl E. Ball, Tarez Samra Graban, and Michelle Sidler
  • ! Ethics, the IRBs, and big data research : toward disciplinary datasets in composition /! Johanna Phelps
  • ! Ethics in big data composition research : cybersecurity and algorithmic accountablitiy as best practices /! Andrew Kulak
  • ! Data do not speak for themselves : interpretation and model selection in unsupervised automated text analysis /! Juho Paakkonen
  • ! "Unsupervised learning" : reflections on a first foray into data-driven argument /! Romeo Garcia
  • ! Making do : working with missing and broken data /! Jill Dahlman.

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