An artificial intelligence tool for heterogeneous team formation in the classroom

J. M. Alberola, E. del Val, Victor Sanchez-Anguix, A. Palomares, M. D. Teruel

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    52 Citations (Scopus)
    191 Downloads (Pure)


    Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin’s role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student’s predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction.
    Original languageEnglish
    Pages (from-to)1-14
    JournalKnowledge-Based Systems
    Early online date11 Mar 2016
    Publication statusPublished - 1 Jun 2016

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems VOL 101, (2016) DOI: 10.1016/j.knosys.2016.02.010

    © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


    • Team formation
    • Artificial intelligence
    • Belbin roles
    • Computational intelligence


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