Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing

Autores
Yannibelli, Virginia Daniela; Amandi, Analia Adriana
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Materia
Collaborative Learning
Collaborative Learning Team Formation
Team Roles
Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Adaptive Evolutionary Algorithms
Simulated Annealing Algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/87560
Acceder
id CONICETDig_10b087090c6f5d7e02539dec74d686f6
oai_identifier_str oai:ri.conicet.gov.ar:11336/87560
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated AnnealingYannibelli, Virginia DanielaAmandi, Analia AdrianaCollaborative LearningCollaborative Learning Team FormationTeam RolesEvolutionary AlgorithmsHybrid Evolutionary AlgorithmsAdaptive Evolutionary AlgorithmsSimulated Annealing Algorithmshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaNational Polytechnic Institute2018-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87560Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing; National Polytechnic Institute; Research in Computing Science; 147; 4; 4-2018; 61-741870-4069CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.semanticscholar.org/paper/Collaborative-Learning-Team-Formation-Considering-Yannibelli-Amandi/ebd178576a314862930badd1bef383fc50960e54info:eu-repo/semantics/altIdentifier/url/https://www.rcs.cic.ipn.mx/2018_147_4/info:eu-repo/semantics/altIdentifier/url/https://www.rcs.cic.ipn.mx/2018_147_4/Collaborative%20Learning%20Team%20Formation%20Considering%20Team%20Roles_%20An%20Evolutionary%20Approach.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:00Zoai:ri.conicet.gov.ar:11336/87560instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:56:00.828CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
title Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
spellingShingle Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
Yannibelli, Virginia Daniela
Collaborative Learning
Collaborative Learning Team Formation
Team Roles
Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Adaptive Evolutionary Algorithms
Simulated Annealing Algorithms
title_short Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
title_full Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
title_fullStr Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
title_full_unstemmed Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
title_sort Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing
dc.creator.none.fl_str_mv Yannibelli, Virginia Daniela
Amandi, Analia Adriana
author Yannibelli, Virginia Daniela
author_facet Yannibelli, Virginia Daniela
Amandi, Analia Adriana
author_role author
author2 Amandi, Analia Adriana
author2_role author
dc.subject.none.fl_str_mv Collaborative Learning
Collaborative Learning Team Formation
Team Roles
Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Adaptive Evolutionary Algorithms
Simulated Annealing Algorithms
topic Collaborative Learning
Collaborative Learning Team Formation
Team Roles
Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Adaptive Evolutionary Algorithms
Simulated Annealing Algorithms
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.
Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
description In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/87560
Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing; National Polytechnic Institute; Research in Computing Science; 147; 4; 4-2018; 61-74
1870-4069
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87560
identifier_str_mv Yannibelli, Virginia Daniela; Amandi, Analia Adriana; Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing; National Polytechnic Institute; Research in Computing Science; 147; 4; 4-2018; 61-74
1870-4069
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.semanticscholar.org/paper/Collaborative-Learning-Team-Formation-Considering-Yannibelli-Amandi/ebd178576a314862930badd1bef383fc50960e54
info:eu-repo/semantics/altIdentifier/url/https://www.rcs.cic.ipn.mx/2018_147_4/
info:eu-repo/semantics/altIdentifier/url/https://www.rcs.cic.ipn.mx/2018_147_4/Collaborative%20Learning%20Team%20Formation%20Considering%20Team%20Roles_%20An%20Evolutionary%20Approach.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv National Polytechnic Institute
publisher.none.fl_str_mv National Polytechnic Institute
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613684798685184
score 13.070432

Publicaciones similares