Using real-time trace data to predict collaboration quality and creative fluency in design teams

Conference paper (2015)

Authors

Ninger Zhou Purdue University

Lorraine Kisselburgh Purdue University

R.S.K. Chandrasegaran Purdue University

Sriram Karthik Badam University of Maryland

Niklas Elmqvist University of Maryland, Indiana University - Purdue University

Kylie Peppler Indiana University - Purdue University

Karthik Ramani Purdue University

Affiliation

External organisation

Creativity Collaboration quality Design learning Sociometrics

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Published Date

2015

Language

English

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Affiliation

External organisation

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.