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Optimizing Peer Teaching to Enhance Team Performance

  • Zheyuan Shi
  • Fei Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10642)

Abstract

Collaboration among human agents with different expertise and capabilities is becoming increasingly pervasive and important for developing new products, providing patient-centered health care, propelling scientific advance, and solving social issues. When the roles of the agents in such collaborative teamwork are highly interdependent, the performance of the team will rely not only on each team member’s individual capabilities but also on their shared understanding and mutual support. Without any understanding in other team members’ area of expertise, the team members may not be able to work together efficiently due to the high cost of communication and the individual decisions made by different team members may even lead to undesirable results for the team. To improve collaboration and the overall performance of the team, the team members can teach each other and learn from each other, and such peer-teaching practice has shown to have great benefit in various domains such as interdisciplinary research collaboration and collaborative health care. However, the amount of time and effort the team members can spend on peer-teaching is often limited. In this paper, we focus on finding the best peer teaching plan to optimize the performance of the team, given the limited teaching and learning capacity. We (i) provide a formal model of the Peer Teaching problem; (ii) present hardness results for the problem in the general setting, and the subclasses of problems with additive utility functions and submodular utility functions; (iii) propose a polynomial time exact algorithm for problems with additive utility function, as well as a polynomial time approximation algorithm for problems with submodular utility functions.

Keywords

Peer teaching Teamwork Optimization 

Notes

Acknowledgments

The research presented in this paper was done when the first author was a visiting student at Massachusetts Institute of Technology, and the second author was a postdoctoral fellow at Harvard University. The research is supported by Harvard Center for Research on Computation and Society fellowship. We thank Barbara Grosz for her comments and helpful discussions.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Swarthmore CollegeSwarthmoreUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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