A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance

[EN] The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abi...

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Detalles Bibliográficos
Autores: Sanchez-Anguix, Víctor|||0000-0003-4851-0037, Julian, Vicente|||0000-0002-2743-6037, Chalumuri, Rithin, Aydogan, Reyhan
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/147518
Acceso en línea:https://riunet.upv.es/handle/10251/147518
Access Level:acceso abierto
Palabra clave:Genetic algorithms
Student-project allocation
Matching
Pareto optimal
Artificial intelligence
LENGUAJES Y SISTEMAS INFORMATICOS
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balanceSanchez-Anguix, Víctor|||0000-0003-4851-0037Julian, Vicente|||0000-0002-2743-6037Chalumuri, RithinAydogan, ReyhanGenetic algorithmsStudent-project allocationMatchingPareto optimalArtificial intelligenceLENGUAJES Y SISTEMAS INFORMATICOSESTADISTICA E INVESTIGACION OPERATIVA[EN] The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors¿ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student¿supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.This work is partially supported by funds of the Faculty of Engineering and Computing at Coventry University, United Kingdom, and funds from EU ICT-20-2015 Project SlideWiki granted by the European Commission.ElsevierInstituto Universitario Mixto de Tecnología de InformáticaDepartamento de Sistemas Informáticos y ComputaciónDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadEscuela Técnica Superior de Ingeniería InformáticaInstituto Universitario Valenciano de Investigación en Inteligencia ArtificialCoventry UniversityEuropean CommissionRepositorio Institucional de la Universitat Politècnica de València Riunet20192019-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/147518reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 H2020 688095 Large-scale pilots for collaborative OpenCourseWare authoring, multiplatform delivery and Learning Analyticsopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1475182026-06-13T07:49:27Z
dc.title.none.fl_str_mv A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
title A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
spellingShingle A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
Sanchez-Anguix, Víctor|||0000-0003-4851-0037
Genetic algorithms
Student-project allocation
Matching
Pareto optimal
Artificial intelligence
LENGUAJES Y SISTEMAS INFORMATICOS
ESTADISTICA E INVESTIGACION OPERATIVA
title_short A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
title_full A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
title_fullStr A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
title_full_unstemmed A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
title_sort A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance
dc.creator.none.fl_str_mv Sanchez-Anguix, Víctor|||0000-0003-4851-0037
Julian, Vicente|||0000-0002-2743-6037
Chalumuri, Rithin
Aydogan, Reyhan
author Sanchez-Anguix, Víctor|||0000-0003-4851-0037
author_facet Sanchez-Anguix, Víctor|||0000-0003-4851-0037
Julian, Vicente|||0000-0002-2743-6037
Chalumuri, Rithin
Aydogan, Reyhan
author_role author
author2 Julian, Vicente|||0000-0002-2743-6037
Chalumuri, Rithin
Aydogan, Reyhan
author2_role author
author
author
dc.contributor.none.fl_str_mv Instituto Universitario Mixto de Tecnología de Informática
Departamento de Sistemas Informáticos y Computación
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Escuela Técnica Superior de Ingeniería Informática
Instituto Universitario Valenciano de Investigación en Inteligencia Artificial
Coventry University
European Commission
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Genetic algorithms
Student-project allocation
Matching
Pareto optimal
Artificial intelligence
LENGUAJES Y SISTEMAS INFORMATICOS
ESTADISTICA E INVESTIGACION OPERATIVA
topic Genetic algorithms
Student-project allocation
Matching
Pareto optimal
Artificial intelligence
LENGUAJES Y SISTEMAS INFORMATICOS
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors¿ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student¿supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/147518
url https://riunet.upv.es/handle/10251/147518
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 H2020 688095 Large-scale pilots for collaborative OpenCourseWare authoring, multiplatform delivery and Learning Analytics
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414608839639040
score 15.301603

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