Learning style preferences of college students using big data
Preferencias de estilo de aprendizaje de estudiantes universitarios que usan big data
Date
2019
2019
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Abstract
Learning styles is one of the most studied topics in the field of education and the research results have generated relevant changes in the teaching-learning process. Currently, there are several theoretical models that explain the characterization and development of learning styles from different points of view, some of them share concepts, while others completely differ. The research focuses on the learning styles of higher education students for improving the quality of the educational process at the university. The results allow the recognize the learning style preferences of college students from different careers, and enable teachers to properly guide the learning activities by selecting the best teaching strategies, thus contributing to raise the quality of education. The results are expected to be relevant for further researches.
Referencias:
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Viloria A., Lis-Gutierrez JP., 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. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham (2018).
A Lee, P Taylor, J Kalpathy-Cramer, A Tufail Machine learning has arrived!. Ophthalmology, 124 (2017), pp. 1726-1728
Yao L (2006). The present situation and development tendency of higher education quality evaluation in Western Countries. Priv. Educ. Beef. (2006).
Gregorutti B, Michel B, Saint-Pierre P (2015) Grouped variable importance with random forests and application to multiple functional data analysis. Comput Stat Data Anal 90:15–35.
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).
Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996)
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).
R. Melero y F. Abad, «Revistas Open Access: Características, modelos económicos y tendencias,» Lámpsakos, pp. 12-23, 2001.
M. Pinto, J. C. J. Alonso, V. Fernández, C. García, J. Garía, C. Gómez, F. Zazo y A.-V. Doucet, «Análisis cualitativo de la visibilidad de la investigación en las Universidaes españolas a través de su página Web,» Rev. Esp. Doc., pp. 345-370, 2004.
M. Torres-Samuel, C. Vásquez, A. Viloria, L. Hernández-Fernandez y R. Portillo-Medina, «Analysis of patterns in the university Word Rankings Webometrics, Shangai, QS and SIR-Scimago: case Latin American» de Lectur Notes in Computer Science (Including subseries Lectur Notes in Artificial Intelligent and Lectur Notes in Bi
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.
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. oinformatics, 2018.
Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: An application for the Honduran banking sector. Innovate: Journal of Science and Technology, 5 (2), 61-75 (2017).
Viloria A., Lis-Gutierrez JP., 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. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham (2018).
A Lee, P Taylor, J Kalpathy-Cramer, A Tufail Machine learning has arrived!. Ophthalmology, 124 (2017), pp. 1726-1728
Yao L (2006). The present situation and development tendency of higher education quality evaluation in Western Countries. Priv. Educ. Beef. (2006).
Gregorutti B, Michel B, Saint-Pierre P (2015) Grouped variable importance with random forests and application to multiple functional data analysis. Comput Stat Data Anal 90:15–35.
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).
Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996)
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).
R. Melero y F. Abad, «Revistas Open Access: Características, modelos económicos y tendencias,» Lámpsakos, pp. 12-23, 2001.
M. Pinto, J. C. J. Alonso, V. Fernández, C. García, J. Garía, C. Gómez, F. Zazo y A.-V. Doucet, «Análisis cualitativo de la visibilidad de la investigación en las Universidaes españolas a través de su página Web,» Rev. Esp. Doc., pp. 345-370, 2004.
M. Torres-Samuel, C. Vásquez, A. Viloria, L. Hernández-Fernandez y R. Portillo-Medina, «Analysis of patterns in the university Word Rankings Webometrics, Shangai, QS and SIR-Scimago: case Latin American» de Lectur Notes in Computer Science (Including subseries Lectur Notes in Artificial Intelligent and Lectur Notes in Bi
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