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Using the Tablet Gestures and Speech of Pairs of Students to Classify Their Collaboration

Abstract This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected

in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as

collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an... (more)
Created Date 2014
Contributor Viswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Subject Computer science / Educational technology / classification / Collaboration / CSCL
Type Masters Thesis
Extent 92 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis