A New Approach for Academic Quality Measurement

Autores
Redchuk, Andrés; Moguerza, Javier M.; Cardone Riportella,Clara Laura
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de trabajo
Estado
versión publicada
Descripción
This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).
Materia
Ingenierías y Tecnologías
Quality Measurement
Postgraduate Programme
Nonparametric Model
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/5148

id CICBA_f41168bf921276366c58dcb3b034eff3
oai_identifier_str oai:digital.cic.gba.gob.ar:11746/5148
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling A New Approach for Academic Quality MeasurementRedchuk, AndrésMoguerza, Javier M.Cardone Riportella,Clara LauraIngenierías y TecnologíasQuality MeasurementPostgraduate ProgrammeNonparametric ModelThis paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).2011-12info:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8042info:ar-repo/semantics/documentoDeTrabajoapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/5148enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:11Zoai:digital.cic.gba.gob.ar:11746/5148Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:11.969CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv A New Approach for Academic Quality Measurement
title A New Approach for Academic Quality Measurement
spellingShingle A New Approach for Academic Quality Measurement
Redchuk, Andrés
Ingenierías y Tecnologías
Quality Measurement
Postgraduate Programme
Nonparametric Model
title_short A New Approach for Academic Quality Measurement
title_full A New Approach for Academic Quality Measurement
title_fullStr A New Approach for Academic Quality Measurement
title_full_unstemmed A New Approach for Academic Quality Measurement
title_sort A New Approach for Academic Quality Measurement
dc.creator.none.fl_str_mv Redchuk, Andrés
Moguerza, Javier M.
Cardone Riportella,Clara Laura
author Redchuk, Andrés
author_facet Redchuk, Andrés
Moguerza, Javier M.
Cardone Riportella,Clara Laura
author_role author
author2 Moguerza, Javier M.
Cardone Riportella,Clara Laura
author2_role author
author
dc.subject.none.fl_str_mv Ingenierías y Tecnologías
Quality Measurement
Postgraduate Programme
Nonparametric Model
topic Ingenierías y Tecnologías
Quality Measurement
Postgraduate Programme
Nonparametric Model
dc.description.none.fl_txt_mv This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).
description This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. The methodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data sets where we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combined with Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a set of dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduate programme. Among the most important conclusion we say the methodology presented in this work has the following advantages: Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student, showing the key factors for improving quality. Student weights can be related to student characteristics to make market segmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can be determined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process or SWOT analysis).
publishDate 2011
dc.date.none.fl_str_mv 2011-12
dc.type.none.fl_str_mv info:eu-repo/semantics/workingPaper
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_8042
info:ar-repo/semantics/documentoDeTrabajo
format workingPaper
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/5148
url https://digital.cic.gba.gob.ar/handle/11746/5148
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
_version_ 1844618607235956736
score 13.070432