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Heterogeneous Teams for Homogeneous Performance

  • Ewa Andrejczuk
  • Filippo Bistaffa
  • Christian Blum
  • Juan A. Rodriguez-Aguilar
  • Carles Sierra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11224)

Abstract

Co-operative learning is used to refer to learning procedures for heterogeneous teams in which individuals and teamwork are organised to complete academic tasks. Key factors of team performance are competencies, personality and gender of team members. Here, we present a computational model that incorporates these key factors to form heterogeneous teams. In addition, we propose efficient algorithms to partition a classroom into teams of even size and homogeneous performance. The first algorithm is based on an ILP formulation. For small problem instances, this approach is appropriate. However, this is not the case for large problems for which we propose a heuristic algorithm. We study the computational properties of both algorithms when grouping students in a classroom into teams.

Notes

Acknowledgements

This work was supported by the CIMBVAL project (funded by MINECO, project number TIN2017-89758-R), 2017 SGR 172, the AppPhil project (funded by RecerCaixa 2017) and Collectiveware (TIN2015-66863-C2-1-R MINECO/FEDER). Bistaffa was supported by the H2020-MSCA-IF-2016 HPA4CF project. The research was partially supported by the ST Engineering - NTU corporate Lab through the NRF corporate lab@university scheme.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ewa Andrejczuk
    • 1
  • Filippo Bistaffa
    • 2
  • Christian Blum
    • 2
  • Juan A. Rodriguez-Aguilar
    • 2
  • Carles Sierra
    • 2
  1. 1.ST Engineering - NTU Corporate Lab, School of Electrical and Electronic Engineering (EEE-NTU)Nanyang Technological UniversitySingaporeSingapore
  2. 2.Artificial Intelligence Research Institute (IIIA-CSIC)BellaterraSpain

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