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Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming he...

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Detalles Bibliográficos
Autores principales: Vallès-Català, Toni, Palau, Ramon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016679/
https://www.ncbi.nlm.nih.gov/pubmed/36920915
http://dx.doi.org/10.1371/journal.pone.0280604
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author Vallès-Català, Toni
Palau, Ramon
author_facet Vallès-Català, Toni
Palau, Ramon
author_sort Vallès-Català, Toni
collection PubMed
description For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming heterogeneous groups of students and to use automated techniques to assist them. In this study, we have created an approach based on complex network theory to design an algorithm called Minimum Entropy Collaborative Groupings (MECG) in order to form these heterogeneous groups more effectively. The algorithm was tested firstly under a synthetic framework and secondly in a real situation. In the first case, we generated 30 synthetic classrooms of different sizes and compared our approach with a genetic algorithm and a random grouping. In the latter case, the approach was tested on a group of 200 students on two subjects of a master’s degree in teacher training. For each subject there were 4 large groups of 50 students each, in which collaborative groups of 4 students were created. Two of these large groups were used as random groups, another group used the CHAEA test and the fourth group used the LML test. The results showed that the groups created with MECG were more effective, had less uncertainty and were more interrelated and mature. It was observed that the randomized groups did not obtain significantly better LML results and that this cannot be related to any emotional or motivational effect because the students performed the test as a placebo measure. In terms of learning styles, the results were significantly better with LML than with CHAEA, whereas no significant difference was observed in the randomized groups.
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spelling pubmed-100166792023-03-16 Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation Vallès-Català, Toni Palau, Ramon PLoS One Research Article For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming heterogeneous groups of students and to use automated techniques to assist them. In this study, we have created an approach based on complex network theory to design an algorithm called Minimum Entropy Collaborative Groupings (MECG) in order to form these heterogeneous groups more effectively. The algorithm was tested firstly under a synthetic framework and secondly in a real situation. In the first case, we generated 30 synthetic classrooms of different sizes and compared our approach with a genetic algorithm and a random grouping. In the latter case, the approach was tested on a group of 200 students on two subjects of a master’s degree in teacher training. For each subject there were 4 large groups of 50 students each, in which collaborative groups of 4 students were created. Two of these large groups were used as random groups, another group used the CHAEA test and the fourth group used the LML test. The results showed that the groups created with MECG were more effective, had less uncertainty and were more interrelated and mature. It was observed that the randomized groups did not obtain significantly better LML results and that this cannot be related to any emotional or motivational effect because the students performed the test as a placebo measure. In terms of learning styles, the results were significantly better with LML than with CHAEA, whereas no significant difference was observed in the randomized groups. Public Library of Science 2023-03-15 /pmc/articles/PMC10016679/ /pubmed/36920915 http://dx.doi.org/10.1371/journal.pone.0280604 Text en © 2023 Vallès-Català, Palau https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Vallès-Català, Toni
Palau, Ramon
Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title_full Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title_fullStr Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title_full_unstemmed Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title_short Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation
title_sort minimum entropy collaborative groupings: a tool for an automatic heterogeneous learning group formation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10016679/
https://www.ncbi.nlm.nih.gov/pubmed/36920915
http://dx.doi.org/10.1371/journal.pone.0280604
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