Mixture structural equation models for classifying university student dropout in latin america

This research seeks to develop a model that allows consider the different forms of heterogeneity of international dropout data and also classify students who continue studying and those who drop out. Specifically, through the use of Mixture Structural Equation Models (MSEM), the study seeks to devel...

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Autores:
Amelec, Viloria
Pineda Lezama, Omar Bonerge
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:11323/5992
Acceso en línea:
http://hdl.handle.net/11323/5992
https://repositorio.cuc.edu.co/
Palabra clave:
Mixture structural equations models
Student satisfaction
Student dropout
Mezcla de modelos de ecuaciones estructurales
Satisfacción del estudiante
Abandono estudiantil
Rights
openAccess
License
CC0 1.0 Universal
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dc.title.spa.fl_str_mv Mixture structural equation models for classifying university student dropout in latin america
dc.title.translated.spa.fl_str_mv Mezcla de modelos de ecuaciones estructurales para clasificar el abandono de estudiantes universitarios en América Latina
title Mixture structural equation models for classifying university student dropout in latin america
spellingShingle Mixture structural equation models for classifying university student dropout in latin america
Mixture structural equations models
Student satisfaction
Student dropout
Mezcla de modelos de ecuaciones estructurales
Satisfacción del estudiante
Abandono estudiantil
title_short Mixture structural equation models for classifying university student dropout in latin america
title_full Mixture structural equation models for classifying university student dropout in latin america
title_fullStr Mixture structural equation models for classifying university student dropout in latin america
title_full_unstemmed Mixture structural equation models for classifying university student dropout in latin america
title_sort Mixture structural equation models for classifying university student dropout in latin america
dc.creator.fl_str_mv Amelec, Viloria
Pineda Lezama, Omar Bonerge
dc.contributor.author.spa.fl_str_mv Amelec, Viloria
Pineda Lezama, Omar Bonerge
dc.subject.spa.fl_str_mv Mixture structural equations models
Student satisfaction
Student dropout
Mezcla de modelos de ecuaciones estructurales
Satisfacción del estudiante
Abandono estudiantil
topic Mixture structural equations models
Student satisfaction
Student dropout
Mezcla de modelos de ecuaciones estructurales
Satisfacción del estudiante
Abandono estudiantil
description This research seeks to develop a model that allows consider the different forms of heterogeneity of international dropout data and also classify students who continue studying and those who drop out. Specifically, through the use of Mixture Structural Equation Models (MSEM), the study seeks to develop a model for classifying dropout and applying it to an international database. The aim is then to determine the classification accuracy degree of the proposed model. The development and application of the model showed that the student´s health, the interpersonal relationships, and class attendance positively influence college adaptation, and in turn college adaptation positively influences college satisfaction. Additionally, the developed model can correctly classify 55.45% of continuing students and 61.68% of students who abandon their careers. These results suggest that the use of MSEM for international databases, characterized by heterogeneity, allows more robust and generalizable studies of dropouts in higher education.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-02-05T13:29:04Z
dc.date.available.none.fl_str_mv 2020-02-05T13:29:04Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 00002010
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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url http://hdl.handle.net/11323/5992
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Ebrahimzadeh, I., Shahraki, A., Shahnaz, A. y Myandoab, A. (2016) Progressing urban development and life quality simultaneously. City, Culture and Society 7, (3), 186-193. 9.
[2] Węziak-Białowolska, D. (2016) Quality of life in cities – Empirical evidence in comparative European perspective. Cities, 58, 87-96. 10.Putra, K. y Sitanggang, J. (2016). The Effect of Public Transport Services on Quality of Life in Medan City. Procedia - Social and Behavioral Sciences, 234, 383-389.
[3] Kubickova, M., Croes, R. y Rivera, M. (2017) Human agency shaping tourism competitiveness and quality of life in developing economies. Tourism Management Perspectives, 22, 120-131.
[4] Biagia, B., Ladub, M., y Meleddub, M. (2018) Analysis Urban Quality of Life and Capabilities: An Experimental Study. Ecological Economics, 150, 137-152
[5] Hu, S. y Das, D. (2018). Quality of life among older adults in China and India: Does productive engagement help? Social Science & Medicine.DOI: //doi.org/10.1016/j.socscimed.2018.06.028.
[6] Wang, Hongying. "New multilateral development banks: Opportunities and challenges for global governance." Global Policy 8.1 (2017): 113- 118.
[7] Amelec, Viloria, and Vasquez Carmen. "Relationship Between Variables of Performance Social and Financial of Microfinance Institutions." Advanced Science Letters 21.6 (2015): 1931-1934.
[8] Bonilla, E. (2017). Posibilidades y límites del crecimiento y desarollo económico-social en países de Europa, Asia, África y América Latina. Revista Questionar, 5, 149-159.
[9] Aroca, P., Gonzalez, P. y Valdebenito, R. (2017). The heterogeneous level of life quality across Chilean regions. Habitat International, 68, 84- 98.
[10] 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).
[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] Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996)
[13] Sekmen, F., Kurkcu, M.: An Early Warning System for Turkey: The Forecasting of Economic Crisis by Using the Artificial Neural Networks. Asian Economic and Financial Review 4(1), 529-43 (2014).
[14] Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach. West Sussex, England: John Wiley & Sons, Ltd. Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).
[15] Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004).
[16] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).
[17] Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (1), 159-75 (2003).
[18] Kuan, C.M.: Artificial neural networks. In the New Palgrave Dictionary of Economics, ed. S.N. Durlauf and L.E Blume. UK: Palgrave Macmillan (2008)
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institution Corporación Universidad de la Costa
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spelling Amelec, Viloriaf68e1040ae8d741a0353699a5e435625Pineda Lezama, Omar Bonergee72941c91bdbbe143e36775e15fb92bd2020-02-05T13:29:04Z2020-02-05T13:29:04Z201900002010http://hdl.handle.net/11323/5992Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This research seeks to develop a model that allows consider the different forms of heterogeneity of international dropout data and also classify students who continue studying and those who drop out. Specifically, through the use of Mixture Structural Equation Models (MSEM), the study seeks to develop a model for classifying dropout and applying it to an international database. The aim is then to determine the classification accuracy degree of the proposed model. The development and application of the model showed that the student´s health, the interpersonal relationships, and class attendance positively influence college adaptation, and in turn college adaptation positively influences college satisfaction. Additionally, the developed model can correctly classify 55.45% of continuing students and 61.68% of students who abandon their careers. These results suggest that the use of MSEM for international databases, characterized by heterogeneity, allows more robust and generalizable studies of dropouts in higher education.Esta investigación busca desarrollar un modelo que permita considerar las diferentes formas de heterogeneidad de los datos de deserción internacional y también clasificar a los estudiantes que continúan estudiando y aquellos que abandonan. Específicamente, mediante el uso de los Modelos de ecuaciones estructurales mixtas (MSEM), el estudio busca desarrollar un modelo para clasificar el abandono y aplicarlo a una base de datos internacional. El objetivo es determinar el grado de precisión de clasificación del modelo propuesto. El desarrollo y la aplicación del modelo mostraron que la salud del estudiante, las relaciones interpersonales y la asistencia a clase influyen positivamente en la adaptación universitaria y, a su vez, la adaptación universitaria influye positivamente en la satisfacción universitaria. Además, el modelo desarrollado puede clasificar correctamente el 55.45% de los estudiantes que continúan y el 61.68% de los estudiantes que abandonan sus carreras. Estos resultados sugieren que el uso de MSEM para bases de datos internacionales, caracterizadas por la heterogeneidad, permite estudios más robustos y generalizables de deserciones en la educación superior.engProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mixture structural equations modelsStudent satisfactionStudent dropoutMezcla de modelos de ecuaciones estructuralesSatisfacción del estudianteAbandono estudiantilMixture structural equation models for classifying university student dropout in latin americaMezcla de modelos de ecuaciones estructurales para clasificar el abandono de estudiantes universitarios en América LatinaArtículo 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] Ebrahimzadeh, I., Shahraki, A., Shahnaz, A. y Myandoab, A. (2016) Progressing urban development and life quality simultaneously. City, Culture and Society 7, (3), 186-193. 9.[2] Węziak-Białowolska, D. (2016) Quality of life in cities – Empirical evidence in comparative European perspective. Cities, 58, 87-96. 10.Putra, K. y Sitanggang, J. (2016). The Effect of Public Transport Services on Quality of Life in Medan City. Procedia - Social and Behavioral Sciences, 234, 383-389.[3] Kubickova, M., Croes, R. y Rivera, M. (2017) Human agency shaping tourism competitiveness and quality of life in developing economies. Tourism Management Perspectives, 22, 120-131.[4] Biagia, B., Ladub, M., y Meleddub, M. (2018) Analysis Urban Quality of Life and Capabilities: An Experimental Study. Ecological Economics, 150, 137-152[5] Hu, S. y Das, D. (2018). Quality of life among older adults in China and India: Does productive engagement help? Social Science & Medicine.DOI: //doi.org/10.1016/j.socscimed.2018.06.028.[6] Wang, Hongying. "New multilateral development banks: Opportunities and challenges for global governance." Global Policy 8.1 (2017): 113- 118.[7] Amelec, Viloria, and Vasquez Carmen. "Relationship Between Variables of Performance Social and Financial of Microfinance Institutions." Advanced Science Letters 21.6 (2015): 1931-1934.[8] Bonilla, E. (2017). Posibilidades y límites del crecimiento y desarollo económico-social en países de Europa, Asia, África y América Latina. Revista Questionar, 5, 149-159.[9] Aroca, P., Gonzalez, P. y Valdebenito, R. (2017). The heterogeneous level of life quality across Chilean regions. Habitat International, 68, 84- 98.[10] 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).[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] Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996)[13] Sekmen, F., Kurkcu, M.: An Early Warning System for Turkey: The Forecasting of Economic Crisis by Using the Artificial Neural Networks. Asian Economic and Financial Review 4(1), 529-43 (2014).[14] Lee, S.-Y. (2007). Structural equation modeling: A Bayesian approach. West Sussex, England: John Wiley & Sons, Ltd. Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).[15] Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004).[16] Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).[17] Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (1), 159-75 (2003).[18] Kuan, C.M.: Artificial neural networks. In the New Palgrave Dictionary of Economics, ed. S.N. Durlauf and L.E Blume. 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