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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 episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when

comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary. / Dissertation/Thesis / Masters Thesis Computer Science 2014

Identiferoai:union.ndltd.org:asu.edu/item:25848
Date January 2014
ContributorsViswanathan, Sree Aurovindh (Author), VanLehn, Kurt (Advisor), T.H CHI, Michelene (Committee member), Walker, Erin (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format92 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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