=Paper=
{{Paper
|id=Vol-1633/ws2-paper6
|storemode=property
|title=Writing Transfer as a Framework for Big Data and Writing Analytics Research
|pdfUrl=https://ceur-ws.org/Vol-1633/ws2-paper6.pdf
|volume=Vol-1633
|authors=Denise Comer
|dblpUrl=https://dblp.org/rec/conf/edm/Comer16
}}
==Writing Transfer as a Framework for Big Data and Writing Analytics Research==
https://ceur-ws.org/Vol-1633/ws2-paper6.pdf
Writing Transfer as a Framework for
Big Data and Writing Analytics Research
Denise Comer
Duke University
Box 90025, Duke University
Durham, NC 27708
011-919-660-4357
comerd@duke.edu
ABSTRACT to explore big-data writing analytics will help illustrate the ways
In this poster presentation, the author will use the frame of writing in which writing-studies scholars can adapt, extend, challenge,
transfer to explore how researchers can transfer strategies, and otherwise make use of this research for other teaching
approaches, and knowledge about writing gained from big-data occasions.
writing analytics to other writing pedagogy contexts. Comer will
share methods and results from the following four big-data 2. BIG DATA, WRITING ANALYTICS AND
research projects stemming from research in her writing based WRITING RESEARCH IN MOOCS:
Massive Open Online Course: 1. Big data and writing assessment;
2. Big-data, writing, and peer-to-peer interactions; 3. Big-data, RESEARCH PROJECT SNAPSHOTS
writing, and negativity; and 4. Big data, peer-review and transfer. 2.1 Big Data & Writing Assessment
These project overviews will be presented as a means of exploring In “Adventuring into MOOC Writing Assessment: Challenges,
the affordances and limitations of using big-data writing analytics Results, and Possibilities,” Denise Comer and Edward M. White
to improve the teaching and learning of writing. researched correlations between peer evaluators and expert
evaluators, and assessed the quality of formative peer feedback.
Keywords Research included a sample size of 100 participants, each of
Big data; MOOCs; Writing Research; Writing Assessment; whom had completed four drafts, four final versions, sixteen peer
Writing Transfer reviews, and three extended self-reflections. Demographic data
included approximately 9,000 survey respondents from course
participants.
1. INTRODUCTION
Big data has become increasingly valuable across many domains
and industries, from social media [1] and science [2] to healthcare 2.2 Big Data & Peer-to-Peer Interactions
[3] and the oil and gas sector [4]. The potential value of big data In “Writing to Learn and Learning to Write Across the
in relation to writing studies is at the frontier of research inquiry, Disciplines: Peer-to-Peer Writing in Introductory MOOCs,”
an emerging area of inquiry. One area where big data and writing Denise Comer, Charlotte R. Clark, and Dorian A. Canelas
studies intersect is Massive Open Online Courses [5]. In 2013, conducted qualitative coding analysis on peer interactions in
Denise Comer, with a team of colleagues and with funding discussion forums to understand how peer interactions impacted
through the Bill & Melinda Gates Foundation, designed a MOOC student learning. The study was multidisciplinary, examining peer
titled English Composition I. It has recently completed its fourth interactions in a writing-based course and in an introductory
iteration and has now enrolled over 270,000 people from around chemistry course. Over 6,800 separate posts were coded. Factors
the world. Comer and colleagues adapted the course in 2016 to fit considered included affect, attitude and emotion, learning gains,
Coursera’s On-Demand platform, wherein the course will be post length, and word frequency. [11]
continually available for weekly enrollment rather than one
session-based beginning. Since 2013, Comer, with several 2.3 Big Data, Writing, & Negativity
research collaborators, has embarked on four distinct research In “Negativity in Massive Open Online Courses: Impacts on
projects using writing analytics and big data from this MOOC (see Learning and Teaching, and How Instructional Teams May Be
citations in sub-sections below). The time is now opportune to Able to Address It,” Denise Comer, Ryan Baker, and Yuan Wang
take stock and consider how those invested in writing studies conducted research into the forms and impacts of negativity
might meaningfully transfer the methods and insights gleaned across a writing-based MOOC and an education MOOC. Research
from this research to other writing pedagogy contexts. This work methods included two case studies, drawing qualitative and
requires a reframing and adaptation of writing transfer knowledge. quantitative data from both course platforms. [12]
Most writing-transfer research is predominately focused on how
writing instructors can incorporate transfer-based pedagogy into
writing pedagogy [6, 7] and/or how we can better understand 2.4 Big Data, Peer Review and Transfer
student capacities with writing transfer [8, 9]. To date, writing In “Providing Peer Feedback as a Site of Writing Transfer,”
transfer research has not often been applied to considerations Denise Comer is conducting qualitative coding on over 6,000
about how writing studies scholars can transfer writing-studies individual comments by students about what they learned about
research methods and insights. Using a transfer-based framework
their own writing and writing projects from having provided peer [3] Jee, K. and Kim, G-H. (2013). Potentiality of big data in the
feedback to others. [13] medical sector: Focus on how to reshape the healthcare
system. Healthcare Informatics Research, 19, 2, 79-85.
[4] Perrons, R. K. and Jensen, J. W. (2015). Data as an asset:
What the oil and gas sector can learn from other industries
3. AFFORDANCES & LIMITATIONS about “big data.” Energy Policy 81, 117-121.
Transferring the concept of affordances from social network sites [5] Krause, S. D. and Lowe, C. (2014). Invasion of the MOOCs:
[14] to understanding big data and writing analytics enables a The promises and perils of massive open online courses.
nuanced understanding of the role of such research in writing Anderson, S.C.: Parlor Press.
studies. Examining social networking sites, danah boyd argues
that the following four affordances play a significant role: [6] Comer, D. (2015). Writing in Transit. Dallas: Fountainhead
persistence, replicability, scalability, and searchability. These Press.
affordances can be usefully extended and adapted to [7] Yancey, K. B., Robertson, L, and Taczak, K. (2014). Writing
understanding big data and writing analytics in writing studies. across contexts. Transfer, composition, and sites of writing.
Limitations of big data in writing studies might be considered in Boulder, CO: Utah State University Press.
the context of limitations of big data in other contexts. In social
[8] Nowacek, R. S. (2011). Agents of integration: Understanding
science research, for instance, big data harbors certain
transfer as a rhetorical act. Carbondale, Ill: Southern Illinois
assumptions about representative sampling, which may not be University Press.
accurate, and researchers must challenge a tendency to position
big data as a panacea research method for all research questions [9] Driscoll, D. L. and Wells, J. (2012). Beyond knowledge and
[15]. Research has also illustrated that big data is limited by skills: Writing transfer and the role of student dispositions.
amorphous definitions and the elision of small patterns of Composition Forum, 26: n. p.
significance [16]. Moreover, another significant limitation of big [10] Comer, D. and White, E. M. (2016). Adventuring into
data research is its potential to instantiate and deepen gaps of MOOC writing assessment: challenges, results, and
privilege and access among scholars in writing studies. possibilities. College Composition and Communication 67, 3,
318-359.
4. BIG DATA, WRITING ANALYTICS [11] Comer, D., Clark, C. R., and Canelas, D. A. (2014). Writing
RESEARCH, & WRITING TRANSFER to learn and learning to write across the disciplines: Peer-to-
peer writing in introductory-level MOOCs. International
Review of Research in Open and Distance Learning 15, 5,
It is important to consider how and whether researchers and 26-82.
teachers can meaningfully transfer big data and writing analytics
among different contexts for writing pedagogy. Any attempts to [12] Comer, D., Baker, R., & Wang, Y. (2015). Negativity in
do so would need to examine opportunities for high-road and low- MOOCs: Impacts on learning and teaching and how
road transfer, as well as positive and negative transfer. Reflection instructional teams may be able to address it. InSight: A
and meta-awareness also provide key components of the Journal of Scholarly Teaching 10, 92-114.
possibilities for transfer related to big data and writing analytic [13] Comer, D. (2016). Providing peer feedback as a site of
research. Researcher and teacher disposition are also integrally writing transfer. Presented at the Conference on College
connected to the ways in which such research might be Composition and Communication, Houston, Texas, March
transferred. And, finally, conceptualizing a vocabulary for 19-22.
understanding the core strategies and skills involved with big-data
[14] Boyd, D. (2010). Social network sites as networked publics:
research and writing analytics would also be a key component of
Affordances, dynamics, and implications.” In Networked
transfer in this area.
self: Identity, community, and culture on social network sites.
Ed. Papacharissi, Z. New York: Routledge, 39-58.
5. REFERENCES
[15] White, P. and Breckenridge, R.S. (2014). Trade-offs,
limitations, and promises of big data in social science
[1] Chan, H. K., Wang, X, and Lacka, E. (2016). A mixed- research. Review of Policy Research 31, 4, 331-338.
method approach to extracting the value of social media data.
Production and Operations Management 25, 3, 568-583. [16] Floridi, L. (2012). Big data and their epistemological
challenge. Philosophy & Technology 25, 4, 435-437.
[2] Kluger, J. (2014). Finding a second Earth. Time 183, 1, 30-
32.