Pair research: Matching people for collaboration, learning, and productivity
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MIller_Pair research.pdf
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409.67 KB
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5b7ddea7881a8d4e8566d4a408f11da0
Author(s)
Miller, Robert C.
Zhang, Haoqi
Gilbert, Eric
Gerber, Elizabeth
Date Issued
February 2014
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dx.doi.org/10.1145/2531602.2531703
