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)


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.



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.


  1. 1.
    Acuña, S.T., Gómez, M., Juristo, N.: How do personality, team processes and task characteristics relate to job satisfaction and software quality? Inf. Softw. Technol. 51(3), 627–639 (2009)CrossRefGoogle Scholar
  2. 2.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications (1993)Google Scholar
  3. 3.
    Alberola, J.M., Del Val, E., Sanchez-Anguix, V., Palomares, A., Teruel, M.D.: An artificial intelligence tool for heterogeneous team formation in the classroom. Knowl.-Based Syst. 101, 1–14 (2016)CrossRefGoogle Scholar
  4. 4.
    Andrejczuk, E., Rodríguez-Aguilar, J.A., Roig, C., Sierra, C.: Synergistic team composition. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1463–1465, International Foundation for Autonomous Agents and Multiagent Systems (2017)Google Scholar
  5. 5.
    Arnold, J., Randall, R.: Work Psychology. Pearson Education Limited, Harlow (2010)Google Scholar
  6. 6.
    Barkley, E.F., Cross, K.P., Major, C.H.: Collaborative Learning Techniques: A Handbook for College Faculty. John Wiley & Sons, Hoboken (2014)Google Scholar
  7. 7.
    Bashshur, M.R., Hernández, A., Peiró, J.M.: The impact of underemployment on individual and team performance. In: Maynard, D., Feldman, D. (eds.) Underemployment, pp. 187–213. Springer, New York (2011). Scholar
  8. 8.
    Briggs, I., Myers, P.B.: Gifts Differing: Understanding Personality Type. Davies-Black Publishing, Mountain View (1995)Google Scholar
  9. 9.
    Chen, B., Chen, X., Timsina, A., Soh, L.: Considering agent and task openness in ad hoc team formation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, 4–8 May 2015, pp 1861–1862 (2015)Google Scholar
  10. 10.
    Davey, L.: If Your Team Agrees on Everything, Working Together is Pointless. Harvard Business Review, Boston (2017)Google Scholar
  11. 11.
    Farhangian, M., Purvis, M.K., Purvis, M., Savarimuthu, B.T.R.: Modeling the effects of personality on team formation in self-assembly teams. In: Chen, Q., Torroni, P., Villata, S., Hsu, J., Omicini, A. (eds.) PRIMA 2015. LNCS (LNAI), vol. 9387, pp. 538–546. Springer, Cham (2015). Scholar
  12. 12.
    Gardner, H.: The theory of multiple intelligences. Ann. Dyslexia 37(1), 19–35 (1987)CrossRefGoogle Scholar
  13. 13.
    IBM.: IBM ILOG CPLEX Optimization Studio (2017)Google Scholar
  14. 14.
    Jung, C.G.: Psychological Types. Princeton University Press, Princeton (1921)Google Scholar
  15. 15.
    Mount, M.K., Barrick, M.R., Stewart, G.L.: Five-factor model of personality and performance in jobs involving interpersonal interactions. Hum. perform. 11(2–3), 145–165 (1998)CrossRefGoogle Scholar
  16. 16.
    Nash, J.: The bargaining problem. Econometrica 18(2), 155–162 (1950)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Okimoto, T., Schwind, N., Clement, M., Ribeiro, T., Inoue, K., Marquis, P.: How to form a task-oriented robust team. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 395–403. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  18. 18.
    Orlin, J.B.: A faster strongly polynomial minimum cost flow algorithm. Oper. Res. 41(2), 338–350 (1993)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Rahwan, T., et al.: Constrained coalition formation. In: Burgard, W., Roth, D., (eds.) AAAI, AAAI Press (2011)Google Scholar
  20. 20.
    Rahwan, T., Michalak, T.P., Wooldridge, M., Jennings, N.R.: Coalition structure generation: a survey. Artif. Intell. 229, 139–174 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs (2015). CoRR, abs/1505.06661Google Scholar
  22. 22.
    Roe, R.A.: Competences-a key towards the integration of theory and practice in work psychology. Gedrag en Organisatie 15(4), 203–224 (2002)Google Scholar
  23. 23.
    Slavin, R.E.: Synthesis of research of cooperative learning. Educ. Leadersh. 48(5), 71–82 (1991)Google Scholar
  24. 24.
    Vosniadou, S.: How Children Learn. Educational Practices Series. International Academy of Education (2001).
  25. 25.
    West, M.A.: Effective Teamwork: Practical Lessons Learned from Organizational Research. Wiley-Blackwell, West Sussex (2012)Google Scholar
  26. 26.
    White, K.B.: Mis project teams: an investigation of cognitive style implications. MIS Q. 8(2), 95–101 (1984)CrossRefGoogle Scholar
  27. 27.
    Wilde, D.J.: Teamology: The Construction and Organization of Effective Teams. Springer, London (2009)Google Scholar
  28. 28.
    Wilde, D.J.: Post-Jungian Personality Theory for Individuals and Teams. SYDROSE LP, (2013)Google Scholar

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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