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

Oscar Revelo Sánchez, César A. Collazos and Miguel A. Redondo
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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

Mathematics, 2021, vol. 9, issue 13, 1-23

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.

Keywords: collaborative learning; collaborative work; genetic algorithms; group formation; personality traits (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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