Mixture structural equation models for classifying university student dropout in latin america
Mezcla de modelos de ecuaciones estructurales para clasificar el abandono de estudiantes universitarios en América Latina
Date
2019
2019
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Abstract
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.
Referencias:
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[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).
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[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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