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Making Sense of Large-Group Discussion Using Automatically Generated RST-Based Explanations

18 Pages Posted: 25 Jan 2015  

Ana Cristina Bicharra Garcia

Universidade Federal Fluminense; Massachusetts Institute of Technology (MIT) - Sloan School of Management

Mark Klein

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Date Written: January 23, 2015

Abstract

With the advances in social media technology, it became possible to involve large group in deliberations. Online discussion environments, such as forums and argumentation maps websites, allow solutions to be outsourced and bred from the crowd. Nevertheless, as a discussion develops, making sense of its content represents a big challenge for newcomers, thus impairing their potential participation. We claim a rhetorically organized text, automatically generated, fosters understanding by guiding participants though the content within chronological, logical and social dimensions of a discussion. An empirical experiment supports our claim. The experiment involved three groups of 16 people whose task was to answer a questionnaire on reading comprehension of a previous debate presented in one of three formats: forum, argumentation map or rhetorically organized text. We discuss the reasons that might explain the results and the implications for the design of large groups’ interaction tools.

Keywords: large-scale deliberation; argument maps; rhetorical structure theory

Suggested Citation

Garcia, Ana Cristina Bicharra and Klein, Mark, Making Sense of Large-Group Discussion Using Automatically Generated RST-Based Explanations (January 23, 2015). Available at SSRN: https://ssrn.com/abstract=2554838 or http://dx.doi.org/10.2139/ssrn.2554838

Ana Cristina Bicharra Garcia

Universidade Federal Fluminense ( email )

Rua Miguel de Frias, 9
Icaraí
Niteroi, Rio DeJaneiro 24220-900
Brazil

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Mark Klein (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

NE20-336
Cambridge, MA 02142
United States
617-253-6796 (Phone)

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