Análisis del alcance de uso de las redes bayesianas, arboles de falla y ecuaciones estructurales

Luz Angélica Aguilar Chávez; Xóchitl Graciela Aguilar Rivas; Manuel Arnoldo Rodríguez Medina; Ericka Berenice Herrera Ríos; Jorge Adolfo Pinto Santos

doi:10.61273/neyart.v2i4.73

Authors

Luz Angélica Aguilar Chávez National Technological Institute of Mexico https://orcid.org/0009-0000-5700-6399
Xóchitl Graciela Aguilar Rivas National Technological Institute of Mexico https://orcid.org/0009-0002-2281-1114
Manuel Arnoldo Rodríguez Medina National Technological Institute of Mexico https://orcid.org/0000-0003-1676-0664
Ericka Berenice Herrera Ríos National Technological Institute of Mexico https://orcid.org/0000-0002-6964-5830
Jorge Adolfo Pinto Santos National Technological Institute of Mexico https://orcid.org/0000-0001-9614-2764

DOI:

https://doi.org/10.61273/neyart.v2i4.73

Keywords:

Bayesian Networks, Structural Equations, Fault Trees, School Performance, Probability

Abstract

Statistical tools such as Bayesian networks, fault trees and structural equations have shown great potential to be used in the modelling and analysis of interest situations in both social and industrial areas. The objective of this paper is to show the application of these three tools together for the analysis of an educational problem, school performance, which is considered to be determined by sociodemographic and student factors specific to the context of individuals. Statistical tools such as structural equations were applied for the validation of the measuring instrument, Bayesian networks as auxiliaries to the calculation of probabilities, and fault trees as evaluations of the risk of occurrence of an event, thus, obtaining the model for the analysis and evaluation of the existing relationships and probabilities to determine a probability of less than 1% for the low academic performance of those students who are in study scenarios and social context favorable for the creation of study environments.

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References

Afifi, A. A., Clark, V. A., & Virginia, C. (1990). Computer-aided Multivariate Analysis (2a ed.). Kluwer Academic.

Aldás, J., Martí, J., Sanz, S., & Ruiz, C. (2013). Key factors ofteenagers’ mobile advertising acceptance. Industrial Management (3 Data Systems), (113), 732–749. DOI: https://doi.org/10.1108/02635571311324179

Anderson, R. T., Skovlund, S. E., Marrero, D., Levine, D. W., Meadows, K., Brod, M., & Balkrishnan, R. (2004). Development and validation of the insulin treatment satisfaction questionnaire. Clinical therapeutics, 26(4), 565–578. DOI: https://doi.org/10.1016/S0149-2918(04)90059-8

Arbuckle J. (2003). Amos user’s guide. SmallWaters Corporation.

Bentler, P. (1995). Los modelos de ecuaciones estructurales y su aplicación en el índice europeo de satisfacción al cliente. Universidad de Zaragoza.

Bustamante, T., Dávila Aragón, A., & Ibarra, G. (2019). Gestión de la tecnología e innovación: un Modelo de Redes Bayesianas. Economía: teoría y práctica, (50), 63-100.

Chonawee, S., Kenyon, C. Y., & Heusler, L. (2006). Cause to Effect Operational Risk Quantification and Management. Palgrave Macmillan Journals, 8(1), 16–42. DOI: https://doi.org/10.1057/palgrave.rm.8250001

Contini, S., & Matuzas, V. (2012). Coupling decomposition and truncation for the analysis of complex fault trees. Journal of Risk and Reliability, 226(3), 249–261. DOI: https://doi.org/10.1177/1748006X11401495

Escobedo Portillo, M. T., Hernández Gómez, J. A., Ortega, V., & Moreno, G. (2016). Modelos de ecuaciones estructurales: Características, fases, construcción, aplicación y resultados. Ciencia & trabajo, 18(55), 16–22. DOI: https://doi.org/10.4067/S0718-24492016000100004

González, C. (2021, julio 9). Análisis árbol de fallas (FTA), qué es y cómo se utiliza – Software GMAO. https://software.aeromarine.es/analisis-arbol-de-fallas-fta-que-es-y-como-se-utiliza/. Aeromarine.

Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. SAGE publications

Kline, R. B. (2023). Principles and practice of structural equation modeling (5a ed.). Guilford Press.

Lopez, G. A., Alonso, I., Mazaira, Z., & Ricardo, H. (2018). Árbol de fallo como herramienta para la mejora de procesos. Estudio de caso cementera XPZ. Revista Espacios, 39(06), 19.

Olmedo-Plata, J. M. (2020). Estilos de aprendizaje y rendimiento académico escolar desde las dimensiones cognitiva, procedimental y actitudinal. Revista de estilos de aprendizaje, (13), 143–159. DOI: https://doi.org/10.55777/rea.v13i26.1540

Pearl, J. (2014). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann. https://doi.org/10.1016/c2009-0-27609-4 DOI: https://doi.org/10.1016/C2009-0-27609-4

Ponce Cumbreras, J., & Gamarra Bustillos, C. (2015). Estilos de Aprendizaje y Rendimiento Académico en Estudiantes de la Universidad María Auxiliadora. Ágora Revista Científica, 2(1), 105–111. https://doi.org/10.21679/arc.v2i1.24 DOI: https://doi.org/10.21679/arc.v2i1.24

Serra-Olivares, J., Muñoz Valverde, C. L., Cejudo Armero, C., & Gil Madrona, P. (2016). Estilos de aprendizaje y rendimiento académico de universitarios de Educación Física chilenos (Learning styles and academic performance of Chilean Physical Education university students). Retos digitales, (32), 62–67. https://doi.org/10.47197/retos.v0i32.51919 DOI: https://doi.org/10.47197/retos.v0i32.51919

Terán Bustamante, A., Dávila Aragón, G., & Castañón Ibarra, R. (2019). Gestión de la tecnología e innovación: un Modelo de Redes Bayesianas. Economía: teoría y práctica, (50), 63-100.