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Pair research: Matching people for collaboration, learning, and productivity

Author(s)
Miller, Robert C.; Zhang, Haoqi; Gilbert, Eric; Gerber, Elizabeth
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Abstract
To increase productivity, informal learning, and collaborations within and across research groups, we have been experimenting with a new kind of interaction that we call {em pair research}, in which members are paired up weekly to work together on each other's projects. In this paper, we present a system for making pairings and present results from two deployments. Results show that members used pair research in a wide variety of ways including pair programming, user testing, brainstorming, and data collection and analysis. Pair research helped members get things done and share their expertise with others.
Date issued
2014-02
URI
http://hdl.handle.net/1721.1/90446
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Robert C. Miller, Haoqi Zhang, Eric Gilbert, and Elizabeth Gerber. 2014. Pair research: matching people for collaboration, learning, and productivity. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW '14). ACM, New York, NY, USA, 1043-1048.
Version: Author's final manuscript
ISBN
9781450325400

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