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Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

Author

Listed:
  • Oscar Revelo Sánchez

    (Galeras.NET Research Group, Universidad de Nariño, San Juan de Pasto 52001, Colombia)

  • César A. Collazos

    (IDIS Research Group, Universidad del Cauca, Popayán 190001, Colombia)

  • Miguel A. Redondo

    (CHICO Research Group, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

In this paper, an approach based on genetic algorithms is proposed to form groups in collaborative learning scenarios, considering the students’ personality traits as a criterion for grouping. This formation is carried out in two stages: In the first, the information of the students is collected from a psychometric instrument based on the Big Five personality model; whereas, in the second, this information feeds a genetic algorithm that is in charge of performing the grouping iteratively, seeking for an optimal formation. The results presented here correspond to the functional and empirical validation of the approach. It is found that the described methodology is useful to obtain groups with the desired characteristics. The specific objective is to provide a strategy that makes it possible to subsequently assess in the context what type of approach (homogeneous, heterogeneous, or mixed) is the most appropriate to organize the groups.

Suggested Citation

  • Oscar Revelo Sánchez & César A. Collazos & Miguel A. Redondo, 2021. "Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model," Mathematics, MDPI, vol. 9(13), pages 1-23, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1578-:d:588574
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    References listed on IDEAS

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    1. Pongcharoen, P. & Hicks, C. & Braiden, P. M. & Stewardson, D. J., 2002. "Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products," International Journal of Production Economics, Elsevier, vol. 78(3), pages 311-322, August.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.

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