Using real-time trace data to predict collaboration quality and creative fluency in design teams
Ninger Zhou (Purdue University)
Lorraine Kisselburgh (Purdue University)
Senthil Chandrasegaran (Purdue University)
S. Karthik Badam (University of Maryland)
Niklas Elmqvist (University of Maryland, Indiana University)
Kylie Peppler (Indiana University)
Karthik Ramani (Purdue University)
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Abstract
In this study, sixteen Engineering students were assigned to small groups (n=4) to work collaboratively on engineering design tasks. Using wearable sociometric devices, we collected real-time non-linguistic speech data on team interaction including turn-taking, successful interrupts and overlaps. Results from 2-stage regression models indicate that speech and conversational dynamics such as turn-taking and successful interrupts are significant in predicting the perceived collaboration quality and creative fluency of design teams.
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