Confronting the Paucity of Collaborative Behavior: Using Large-Scale Content Analysis to Define and Measure Student Collaboration in U.S., K-12 Wikis

26 Pages Posted: 4 Feb 2013  

Justin Reich

Massachusetts Institute of Technology (MIT) - Office of Digital Learning

Date Written: February 2, 2013

Abstract

This study investigates the distribution of student collaborative behaviors in a sample of 406 U.S., K-12 wikis randomly drawn from a population of 179,851 publicly-viewable, education-related wikis. Aided by computational tools, trained human coders conducted a large-scale content analysis examining every revision to every page of each of these 406 wikis. Seven types of student collaborative behavior were found in these wikis: concatenation, copyediting, co-construction, commenting, discussion, scheduling, and planning. These behaviors occurred very infrequently; only 11% of wikis show evidence of even one of these types of behaviors, and the simplest forms of collaboration, concatenation and commenting were most common. These findings suggest that peer-production platforms used in schools primarily support individual work, especially given the emphasis on individual assessment in formal school settings. Confronting the paucity of student collaboration in peer-production platforms used in educational settings is an important challenge for the learning sciences. The paper concludes with a discussion of how methods leveraging large unstructured datasets can complement more established methods in the learning sciences and contribute to theory-testing, context-setting, and theory-building.

Keywords: Collaboration, Wikis, Content Analysis, Taxonomy, education, education technology, big data

Suggested Citation

Reich, Justin, Confronting the Paucity of Collaborative Behavior: Using Large-Scale Content Analysis to Define and Measure Student Collaboration in U.S., K-12 Wikis (February 2, 2013). Available at SSRN: https://ssrn.com/abstract=2210949 or http://dx.doi.org/10.2139/ssrn.2210949

Justin Reich (Contact Author)

Massachusetts Institute of Technology (MIT) - Office of Digital Learning ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

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