MODELO DE ECUACIÓN ESTRUCTURAL EXPLICATIVO DEL DESEMPEÑO ESTUDIANTIL EN LA ASIGNATURA DE ESTADÍSTICA GENERAL EN LA UNALM

Rolando Salazar Vega; Fernando Rosas Villena

doi:10.21704/ac.v82i1.1744

Authors

Rolando Salazar Vega Facultad de Economía y Planificación, Universidad Nacional Agraria La Molina, La Molina, 15024, Lima, Perú.
Fernando Rosas Villena Rosas Villena https://orcid.org/0000-0002-4992-4971

DOI:

https://doi.org/10.21704/ac.v82i1.1744

Keywords:

Structural Equation Modeling, student performance, teacher performance, personal perception, past performance

Abstract

The purpose of the study is to measure the adjustment of an explanatory model of student performance in the subject of General Statistics at the La Molina National Agrarian University (UNALM) through the statistical technique of Structural Equation Models. The proposed model considers four factors: student performance, teacher performance, personal perception, and past performance. Two assessment instruments are applied in the study, first to measure personal perception, and the second, is to measure teacher performance. Cronbach's Alpha Reliability Coefficient is used to verify the reliability requirement and the Confirmatory Factor Analysis to verify the validity requirement of the assessment instruments. In both assessment instruments indicators obtain to confirm their reliability and validity. The fit of the proposed model was verified using the goodness of fit indicators, finding that the proposed model had a good fit. Said adjustment improved through a process of modifying the model through the inclusion of the relationship that indicates that personal perception depends on past performance. It verified the modified model that among the factors: past performance, teacher performance, and personal perception, the former is a predictor of student’s performance. Also, in the modified model, where a new relationship includes, which was a past performance that explains the personal perception, this last relationship and the one that indicates that teacher performance explains personal perception were significant.

Downloads

Download data is not yet available.

References

• Aldás, J., & Uriel, E. (2017). Análisis multivariante aplicado con R. 2da Edición. Ediciones Paraninfo, S.A., Madrid, España, 531 p.

• Cruz, M. (2016). Factores que influyen en el rendimiento académico del estudiante. Escenarios: empresa y territorio, 5(5), 113.

• Flores, Y., Lugo, G., Ramirez, T., Brito, F., & Crespo, V. (2019). Factores asociados al rendimiento académico de los estudiantes en la Facultad de Odontología de la Universidad Central de Venezuela diseño y validación de un cuestionario, Acta Odontológica Venezolana, 57(2), 6.

• Garbanzo, G. (2007). Factores asociados al rendimiento académico en estudiantes universitarios, una reflexión desde la calidad de la educación superior pública. Educación, 31(1), 58

• Gargallo, B., Garfella, P., Sánchez, F., Ros, C., & Serra, B. (2009). La influencia del autoconcepto en el rendimiento académico en estudiantes universitarios, REOP, 20(1), 18, 24.

• Guzmán, M. (2012). Modelos predictivos y explicativos del rendimiento académico universitario: caso de una institución privada en México. Memoria para optar el grado de Doctor. Universidad Complutense de Madrid, Madrid, España. 58, 118-119 p.

• Hair, J., Anderson, R., Tatham, R., & Black, W. (1999). Análisis Multivariante. 5ta Edición. Prentice Hall Iberia, S.R.L., Madrid, España, 620-683 p.

• Muelas, A. (2013). Influencia de la variable de personalidad en el rendimiento académico de los estudiantes cuando finalizan la Educación Secundaria Obligatoria (ESO) y comienzan Bachillerato, Historia y Comunicación Social, 18 (Esp.), 123.

• Schumacker, R., & Lomax, R. (2016). A Beginner´s Guide to Structural Equation Modeling. 4ta edición. Taylor & Francis Group. New York, Estados Unidos. 108-109 p.

• Véliz, C. (2016). Análisis multivariante.1ra Edición. Cengage Learning. Lima, Perú, 173-174 p.