[Amanda Licastro]

Please consider submitting a proposal for the following edited collection. Feel free to share widely (with apologies for cross-posting).

This edited collection, currently under consideration, will serve as a research and methods guide for practitioners interested in conducting large-scale data-driven examinations of writing.
Full CFP here: http://bit.ly/comp-as-data-cfp (which leads to http://digitocentrism.com/digital-projects/cfp-composition-as-big-data/ )
Computational analysis of big data has changed the way information is processed. Corporations analyze patterns in what people buy, how far they run, where they spend their time; they quantify habits to create more effective advertisements and cross-promotions. In academe, humanities scholars are using computational analysis to identify patterns in literary texts, historical documents, image archives, and sound, all of which has added to the body of knowledge in humanities theory and methodology. Meanwhile, many institutions and writing programs are adopting learning management systems that may digitally archive hundreds – if not thousands or tens of thousands – of student compositions from across levels and disciplines. What is our responsibility, and what is the potential, in harnessing big-data methods as composition researchers, teachers, and administrators?

Composition and rhetoric scholars have begun to adopt corpus-based computational analysis both to better understand the field as a whole – through the rhetoric of job postings (Lauer), professional journals (Mueller; Almjeld et al), and dissertation records (Miller; Gatta) – and to research student compositions, the teaching of which is the primary job of most composition and rhetoric scholars. Through data-driven studies of student entrance exams (Aull), citation practices (Jamieson and Moore Howard), revision practices (Moxley), and acknowledgment of counterarguments (Lancaster), scholars have found patterns that distinguish student writing from published academic writing, suggesting areas to target for instruction.

This edited collection will model and reflect on the research made possible by high-capacity data storage and computation, either alone or in conjunction with close reading and evaluation in context. Authors are invited to submit abstracts for chapters that focus on the rhetoric, methods, and findings of recent large-scale data studies of writing. We are especially interested in contributions that include replicable practices and/or detailed descriptions of method, with an eye toward graduate-level research, teaching, or administrative applications in the intersecting fields of digital humanities, linguistics, and composition.

The following list of topics and questions is not exhaustive, but suggestive, illustrating the range of issues to be taken up:

•    Data Capture and the Captivation of Data
◦    When we say “big data” in composition what do we mean? What datasets are available, promising, or already producing insight?
◦    What new questions do these datasets allow us to ask or answer? What are their limitations?
◦    How has data gathered from large corpora of (student) writing changed the scholarship and practice of composition / rhetoric? How might such data do so in the future?
•    Responsible Research
◦    Who is responsible for creating or curating datasets in composition? How might the answers change at different scales?
◦    What are the ethical responsibilities of anyone storing, retrieving, or analyzing composition data – perhaps especially where students and their writing are concerned?
◦    How, should researchers negotiate issues of consent and representation when recording or reporting on data? How is this affected by the scale or scope of the data?
•    Discourse and Discovery
◦    How can computational tools aid in the qualitative coding of (student) writing? How do these practices relate to traditional coding methods?
◦    What data-supported models of writing practices emerge from the study of digital corpora?
◦    What does or can big data show about the nature of expertise and learning in the context of composing?
•    Pedagogical Practices
◦    How can the field of composition / rhetoric use data to positively impact pedagogical or andragogical practices? For example, how can data-supported studies improve composition instruction in higher education?
◦    What is the relationship between distant and close reading in regard to assessing student writing? Can and/or should distant reading practices be applied to assessment at the undergraduate level, and in what ways?
◦    What role can analysis of big data play for student researchers in composition / rhetoric?
•    Supporting a Data-Supported Future
◦    What standards or best practices are emerging for data archiving, aggregation, and interoperability?
◦    How might those new to big-data approaches most usefully manage issues of scope or  documentation?
◦    How can we best support new researchers, teachers, or administrators in developing comfort with big-data approaches and insights? What does a successful program of big-data training look like?

Abstracts of approximately 350 words should provide, in as much detail as possible, the focus and argument(s) for the proposed chapter. Abstracts and brief author bios are due 1 August 2017 via Google Forms at http://bit.ly/comp-as-data. Questions can be directed to Amanda Licastro (amanda.licastro@gmail.com) or Ben Miller (benmiller314@gmail.com) with the subject line “Composition as Big Data.”

Thank you,

Amanda Licastro, PhD
Assistant Professor of Digital Rhetoric,
Stevenson University in Maryland

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