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
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/5148
Ver los metadatos del registro completo
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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 |
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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 |
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1844618607235956736 |
score |
13.070432 |