Beyond Team Makeup: Diversity in Teams Predicts Valued Outcomes in Computer-Mediated Collaborations

要旨

In an increasingly globalized and service-oriented economy, people need to engage in computer-mediated collaborative problem solving (CPS) with diverse teams. However, teams routinely fail to live up to expectations, showcasing the need for technologies that help develop effective collaboration skills. We take a step in this direction by investigating how different dimensions of team diversity (demographic, personality, attitudes towards teamwork, prior domain experience) predict objective (e.g. effective solutions) and subjective (e.g. positive perceptions) collaborative outcomes. We collected data from 96 triads who engaged in a 30-minute CPS task via videoconferencing. We found that demographic diversity and differing attitudes towards teamwork predicted impressions of positive engagement, while personality diversity predicted learning outcomes. Importantly, these relationships were maintained after accounting for team makeup. None of the diversity measures predicted task performance. We discuss how our findings can be incorporated into technologies that aim to help diverse teams develop CPS skills.

キーワード
Diversity
team makeup
collaborative problem solving
learning technologies
著者
Angela E.B. Stewart
University of Colorado Boulder, Boulder, CO, USA
Mary Jean Amon
University of Central Florida, Orlando, FL, USA
Nicholas D. Duran
Arizona State University, Glendale, AZ, USA
Sidney K. D'Mello
University of Colorado Boulder, Boulder, CO, USA
DOI

10.1145/3313831.3376279

論文URL

https://doi.org/10.1145/3313831.3376279

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Equity & values in learning systems & activities

Paper session
313A O'AHU
5 件の発表
2020-04-30 01:00:00
2020-04-30 02:15:00
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