Model and simulation of structural equations for determining the student satisfaction

Structural Equations Models (SEM) determine the dependence or independence relationship of the variables through the integration of linear equations. These models combine factorial analysis with linear regression to determine the data adjustment obtained with a proposed model by means of a path anal...

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Autores:
Amelec, Viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5991
Acceso en línea:
https://hdl.handle.net/11323/5991
https://repositorio.cuc.edu.co/
Palabra clave:
Structural equations
Maximum likelihood method
Factor analysis
Learning
Management
System
TPACK model
Ecuaciones estructurales
Método de máxima verosimilitud
Análisis factorial
Sistema de gestión del aprendizaje
Modelo TPACK
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/5991
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv Model and simulation of structural equations for determining the student satisfaction
dc.title.translated.spa.fl_str_mv Modelo y simulación de ecuaciones estructurales para determinar la satisfacción del alumno
title Model and simulation of structural equations for determining the student satisfaction
spellingShingle Model and simulation of structural equations for determining the student satisfaction
Structural equations
Maximum likelihood method
Factor analysis
Learning
Management
System
TPACK model
Ecuaciones estructurales
Método de máxima verosimilitud
Análisis factorial
Sistema de gestión del aprendizaje
Modelo TPACK
title_short Model and simulation of structural equations for determining the student satisfaction
title_full Model and simulation of structural equations for determining the student satisfaction
title_fullStr Model and simulation of structural equations for determining the student satisfaction
title_full_unstemmed Model and simulation of structural equations for determining the student satisfaction
title_sort Model and simulation of structural equations for determining the student satisfaction
dc.creator.fl_str_mv Amelec, Viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
dc.contributor.author.spa.fl_str_mv Amelec, Viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
dc.subject.spa.fl_str_mv Structural equations
Maximum likelihood method
Factor analysis
Learning
Management
System
TPACK model
Ecuaciones estructurales
Método de máxima verosimilitud
Análisis factorial
Sistema de gestión del aprendizaje
Modelo TPACK
topic Structural equations
Maximum likelihood method
Factor analysis
Learning
Management
System
TPACK model
Ecuaciones estructurales
Método de máxima verosimilitud
Análisis factorial
Sistema de gestión del aprendizaje
Modelo TPACK
description Structural Equations Models (SEM) determine the dependence or independence relationship of the variables through the integration of linear equations. These models combine factorial analysis with linear regression to determine the data adjustment obtained with a proposed model by means of a path analysis, which represents the relationship between latent and observed variables. Observed variables are those that can be directly measured, usually through questionnaires. Latent variables are not directly measured and can be endogenous (dependent) or exogenous (independent). This research provides a model that allows to determine student satisfaction through the structural equations modeling by using the Technological Pedagogical Content Knowledge model (TPACK).
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-02-05T13:28:52Z
dc.date.available.none.fl_str_mv 2020-02-05T13:28:52Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv 00002010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5991
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).
[2] Garson, D. (2013). Factor analysis. Asheboro. North Caroline State: Blue Books. University Press.
[3] Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Annals of Operations Research 168 (1), 151–168 (2009).
[4] Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).
[5] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).
[6] Oviedo, B. a. (2015). Análisis de datos educativos utilizando redes bayesianas, Latin American and Caribbean Conference for Engineering and Technology LACCEI 2015.
[7] Hair, Joseph; Anderson, Rolph; Tatham, Ronald y Black, W. (2001). Análisis multivariante. Madrid, España: Prentice Hall.
[8] Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).
[9] Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham , vol 10943, 1-12 (2018).
[10] Samperio Pacheco, Víctor Manuel. (2019). Ecuaciones estructurales en los modelos educativos: características y fases en su construcción. Apertura, 11(1), pp. 90-103. http://dx.doi. org/10.32870/Ap.v11n1.1402).
[11] Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J. : Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching – Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data.
[12] Roig-Vila, Rosabel; Mengual-Andrés, Santiago & Quinto-Medrano, Patricio. (2015). Conocimientos tecnológicos, pedagógicos y disciplinares del profesorado de primaria. Comunicar, XXII(45), pp. 151-159. https://doi.org/10.3916/C45-2015-16
[13] Vanyolos, E., I. Furka, I. Miko y otros tres autores, How does practice improve the skills of medical students during consecutive training courses? doi; https://dx.doi.org/10.1590/s0102-865020170060000010. Rev. Acta Cirurgica Brasileira, 32(6), 491-502 (2017)
[14] Tárraga Mínguez, Raúl; Sanz Cervera, Pilar; Pastor Cerezuela, Gemma y Fernández Andrés, María. (2017). Análisis de la autoeficacia percibida en el uso de las TIC de futuros maestros y maestras de educación infantil y educación primaria. Revista Electrónica Interuniversitaria de Formación del Profesorado, 20(3), pp. 107-116. https://doi.org/10.6018/reifop.20.3.263901.
[15] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).
[16] Kline, Rex. (2005). Principles and practice of structural equation modeling. Nueva York: Gilford Press
[17] Cejas León, Roberto; Navío Gámez, Antonio y Barroso Osuna, Julio. (2016). Las competencias del profesorado universitario desde el modelo TPACK (conocimiento tecnológico y pedagógico del contenido). Pixel-Bit. Revista de Medios y Educación, (49), pp. 105-119. https://doi.org/10.12795/pixelbit.2016.i49.07
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dc.publisher.spa.fl_str_mv Procedia Computer Science
institution Corporación Universidad de la Costa
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spelling Amelec, ViloriaPineda Lezama, Omar BonergeMercado Caruso, Nohora Nubia2020-02-05T13:28:52Z2020-02-05T13:28:52Z201900002010https://hdl.handle.net/11323/5991Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Structural Equations Models (SEM) determine the dependence or independence relationship of the variables through the integration of linear equations. These models combine factorial analysis with linear regression to determine the data adjustment obtained with a proposed model by means of a path analysis, which represents the relationship between latent and observed variables. Observed variables are those that can be directly measured, usually through questionnaires. Latent variables are not directly measured and can be endogenous (dependent) or exogenous (independent). This research provides a model that allows to determine student satisfaction through the structural equations modeling by using the Technological Pedagogical Content Knowledge model (TPACK).Los modelos de ecuaciones estructurales (SEM) determinan la relación de dependencia o independencia de las variables a través de la integración de ecuaciones lineales. Estos modelos combinan análisis factorial con regresión lineal para determinar el ajuste de datos obtenido con un modelo propuesto mediante un análisis de ruta, que representa la relación entre las variables latentes y observadas. Las variables observadas son aquellas que pueden medirse directamente, generalmente a través de cuestionarios. Las variables latentes no se miden directamente y pueden ser endógenas (dependientes) o exógenas (independientes). Esta investigación proporciona un modelo que permite determinar la satisfacción de los estudiantes a través del modelado de ecuaciones estructurales mediante el uso del modelo de Conocimiento de Contenido Pedagógico Tecnológico (TPACK).Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda Lezama, Omar BonergeMercado Caruso, Nohora Nubia-will be generated-orcid-0000-0001-9261-8331-600engProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Structural equationsMaximum likelihood methodFactor analysisLearningManagementSystemTPACK modelEcuaciones estructuralesMétodo de máxima verosimilitudAnálisis factorialSistema de gestión del aprendizajeModelo TPACKModel and simulation of structural equations for determining the student satisfactionModelo y simulación de ecuaciones estructurales para determinar la satisfacción del alumnoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).[2] Garson, D. (2013). Factor analysis. Asheboro. North Caroline State: Blue Books. University Press.[3] Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Annals of Operations Research 168 (1), 151–168 (2009).[4] Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).[5] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).[6] Oviedo, B. a. (2015). Análisis de datos educativos utilizando redes bayesianas, Latin American and Caribbean Conference for Engineering and Technology LACCEI 2015.[7] Hair, Joseph; Anderson, Rolph; Tatham, Ronald y Black, W. (2001). Análisis multivariante. Madrid, España: Prentice Hall.[8] Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).[9] Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham , vol 10943, 1-12 (2018).[10] Samperio Pacheco, Víctor Manuel. (2019). Ecuaciones estructurales en los modelos educativos: características y fases en su construcción. Apertura, 11(1), pp. 90-103. http://dx.doi. org/10.32870/Ap.v11n1.1402).[11] Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J. : Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching – Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data.[12] Roig-Vila, Rosabel; Mengual-Andrés, Santiago & Quinto-Medrano, Patricio. (2015). Conocimientos tecnológicos, pedagógicos y disciplinares del profesorado de primaria. Comunicar, XXII(45), pp. 151-159. https://doi.org/10.3916/C45-2015-16[13] Vanyolos, E., I. Furka, I. Miko y otros tres autores, How does practice improve the skills of medical students during consecutive training courses? doi; https://dx.doi.org/10.1590/s0102-865020170060000010. Rev. Acta Cirurgica Brasileira, 32(6), 491-502 (2017)[14] Tárraga Mínguez, Raúl; Sanz Cervera, Pilar; Pastor Cerezuela, Gemma y Fernández Andrés, María. (2017). Análisis de la autoeficacia percibida en el uso de las TIC de futuros maestros y maestras de educación infantil y educación primaria. Revista Electrónica Interuniversitaria de Formación del Profesorado, 20(3), pp. 107-116. https://doi.org/10.6018/reifop.20.3.263901.[15] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).[16] Kline, Rex. (2005). Principles and practice of structural equation modeling. Nueva York: Gilford Press[17] Cejas León, Roberto; Navío Gámez, Antonio y Barroso Osuna, Julio. (2016). Las competencias del profesorado universitario desde el modelo TPACK (conocimiento tecnológico y pedagógico del contenido). Pixel-Bit. Revista de Medios y Educación, (49), pp. 105-119. https://doi.org/10.12795/pixelbit.2016.i49.07PublicationORIGINALModel and Simulation of Structural Equations for Determining the.pdfModel and Simulation of Structural Equations for Determining the.pdfapplication/pdf460285https://repositorio.cuc.edu.co/bitstreams/d5c3044c-7831-467b-a0e7-2ee313d072ec/download0585a053f7e1305a56b0b113156f965fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/e07b7ecb-5bf6-486e-90f6-c8bfbf1b58cf/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/31ebc0b4-a563-4ef9-bd7e-8cf0533deb8f/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILModel and Simulation of Structural Equations for Determining the.pdf.jpgModel and Simulation of Structural Equations for Determining the.pdf.jpgimage/jpeg45826https://repositorio.cuc.edu.co/bitstreams/6b5c8c38-aa42-4dfe-b6f8-cebf54b16ce7/download8e9da15a11af04953cfaec367c366319MD55TEXTModel and Simulation of Structural Equations for Determining the.pdf.txtModel and Simulation of Structural Equations for Determining the.pdf.txttext/plain17437https://repositorio.cuc.edu.co/bitstreams/70c05595-5313-4382-862f-bd60da9b0516/download02c2272e59f8d031b1a0ad9f2fd91a8fMD5611323/5991oai:repositorio.cuc.edu.co:11323/59912024-09-17 12:50:07.438http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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