Impact of socio-demographic variables on university students' perception of online education post-COVID-19
DOI:
https://doi.org/10.5944/ried.28.2.43294Keywords:
sociodemographic variables, online education, student perceptions, ANOVA permutation, higher educationAbstract
The digital transformation of present-day society has, along with the impacts of COVID-19, led university students to prefer online education, and the need to recognize the factors that influence its effectiveness and perceived quality has, therefore, emerged. The objective of the present study was to determine the effect that the socio-demographic variables of academic level, gender, area of residence and age have on the perceptions of online Bachelor’s degree students at the Technical University of Manabi with regards to the components “pedagogical, technological and social organization of the online teaching-learning process” (PC1), “learning achievements” (PC2), “assessment and feedback” (PC3), and the “design of the course or subject” (PC4) in the Post-COVID-19 context. Descriptive-correlational-type research with a quantitative focus was developed, in which 545 students responded to a questionnaire adapted to and validated for the context of Ecuador. The results of the PERMANOVA and the pairwise effects evidenced the statistically significant impact of socio-demographic variables such as academic level, gender and age on the students’ perceptions of the principle components of online education. Specifically, academic level had an influence on PC1 and PC3, while gender affected PC4. Finally, the main finding of this study was that students of 26 years of age and over have a more favorable perception when compared to that of those who are younger. The results obtained provide an empirical basis on which to improve online education in the Post-COVID-19 era, making it more effective, inclusive, equitable and adapted.
Downloads
References
Amaning, N. (2024). Qualitative analysis on technology acceptance model of accounting faculty perceptions of virtual accounting coursework. Cogent Education, 11(1), 2331345. https://doi.org/10.1080/2331186X.2024.2331345
Anderson, M. J. (2001). A new method for non‐parametric multivariate analysis of variance. Austral Ecology, 26(1), 32-46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x
Arslan, M., Edirne, T., Emre, N., & Ozsahin, A. (2023). Medical Students’ Perceptions of Online Education during the COVID-19 Pandemic. Eurasian Journal of Family Medicine, 12(1), 1-10. https://doi.org/10.33880/ejfm.2023120101
Avendaño, W. R., Luna, H. O., & Rueda, G. (2021). Educación virtual en tiempos de COVID-19: percepciones de estudiantes universitarios. Formación Universitaria, 14(5), 119-128. https://doi.org/10.4067/S0718-50062021000500119
Barrientos Oradini, N., Yáñez Jara, V., Pennanen-Arias, C., & Aparicio Puentes, C. (2022). Análisis sobre la educación virtual, impactos en el proceso formativo y principales tendencias. Revista de Ciencias Sociales, 28(4), 496-511. https://doi.org/10.31876/rcs.v28i4.39144
Bazán-Ramírez, A., Capa-Luque, W., Ango-Aguilar, H., Anaya-González, R., & Cárdenas-López, V. (2023). Perception of Peruvian Students Studying in Biological Sciences about the Advantages of Virtual Classes during the COVID-19. Pandemic. Education Sciences, 13(6), 626. https://doi.org/10.3390/educsci13060626
Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B: Statistical Methodology, 57(1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261-304. https://doi.org/10.1177/0049124104268644
Cabero-Almenara, J., Valencia-Ortiz, R., Llorente-Cejudo, C., & Palacios-Rodríguez, A. de P. (2023). Nativos e inmigrantes digitales en el contexto de la COVID-19: las contradicciones de una diversidad de mitos. Texto Livre, 16, e42233. https://doi.org/10.1590/1983-3652.2023.42233
Carbonell García, C. E., Rodríguez Román, R., Sosa Aparicio, L. A., & Alva Olivos, M. A. (2021). De la educación a distancia en pandemia a la modalidad hibrida en pospandemia. Revista Venezolana de Gerencia, 26(96), 1154-1171. https://doi.org/10.52080/rvgluz.26.96.10
Colvin, R. G., Delcoure, N., Krueger, T. M., & Singh, H. (2024). The Impact of University Student Services and Student Life Characteristics on Students’ Perceptions of Online Education. Journal of Higher Education Theory and Practice, 24(1), 186-199. https://doi.org/10.33423/jhetp.v24i1.6770
Consejo de Educación Superior (2023). Reglamento de Régimen Académico. Quito, Ecuador.
Cramarenco, R. E., Burcă-Voicu, M. I., & Dabija, D. C. (2023). Student Perceptions of Online Education and Digital Technologies during the COVID-19 Pandemic: A Systematic Review. Electronics, 12(2), 319. https://doi.org/10.3390/electronics12020319
Crisol-Moya, E., Herrera-Nieves, L., & Montes-Soldado, R. (2020). Educación virtual para todos: una revisión sistemática. Education in the Knowledge Society (EKS), 21, 13. https://doi.org/10.14201/eks.23448
Cruz, I. R. (2023). Análisis bibliométrico de la educación virtual y rendimiento académico universitario periodo 1996-2021, utilizando datos Scopus. RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação, (52), 126-141. https://doi.org/10.17013/risti.52.126-141
Dinu, L. M., Baykoca, A., Dommett, E. J., Mehta, K. J., Everett, S., Foster, J. L. H., & Byrom, N. C. (2022). Student Perceptions of Online Education during COVID-19 Lockdowns: Direct and Indirect Effects on Learning. Education Sciences, 12(11), 813. https://doi.org/10.3390/educsci12110813
Ernst, M. D. (2004). Permutation Methods: A Basis for Exact Inference. Statistical Science, 19(4), 676-685. https://doi.org/10.1214/088342304000000396
Estrada, L., & Paz Delgado, C. L. (2022). Enseñanza remota de emergencia: Estudio de satisfacción en estudiantes universitarios durante la pandemia por COVID-19. Perfiles Educativos, 44(178), 46-62. https://doi.org/10.22201/iisue.24486167e.2022.178.60503
Estrada-Araoz, E. G., Gallegos Ramos, N. A., Paredes Valverde, Y., Quispe Herrera, R., & Mori Bazán, J. (2023). Perception of Peruvian university students about virtual education in the context of the COVID-19 pandemic. Archivos Venezolanos de Farmacología y Terapéutica, 41(9). http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/25908
Ferrer-Cascales, R., Walker, S. L., Reig-Ferrer, A., Fernández-Pascual, M. D., & Albaladejo-Blázquez, N. (2011). Evaluation of hybrid and distance education learning environments in Spain. Australasian Journal of Educational Technology, 27(7). https://doi.org/10.14742/ajet.906
García, S., & Herrera, F. (2008). An extension on Statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. Journal of Machine Learning Research, 9(12). https://www.jmlr.org/papers/volume9/garcia08a/garcia08a.pdf
García Aretio, L. (2004). Viejos y nuevos modelos de educación a distancia. Bordón. Revista de Pedagogía, 56(3-4), 409-429
García Aretio, L. (2020). Bosque semántico: ¿educación/enseñanza/aprendizaje a distancia, virtual, en línea, digital, eLearning…? RIED. Revista Iberoamericana de Educación a Distancia, 23(1), 9-28. https://doi.org/10.5944/ried.23.1.25495
Garzón Umerenkova, A., & Gil Flores, J. (2017). Gestión del tiempo en alumnado universitario con diferentes niveles de rendimiento académico. Educação e Pesquisa, 44, e157900. https://doi.org/10.1590/s1678-4634201708157900
Gómez-Arteta, I., Escobar-Mamani, F., & Bonifaz Valdez, B. (2024). Contexto geográfico versus tecnología: una pugna que determina la satisfacción estudiantil con la educación virtual universitaria en Puno-Perú. RELIEVE-Revista Electrónica de Investigación y Evaluación Educativa, 30(1). https://doi.org/10.30827/relieve.v30i1.25133
Good, P. (2000). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer. https://doi.org/10.1007/978-1-4757-3235-1
Harpe, S. E. (2015). How to analyze Likert and other rating scale data. Currents in Pharmacy Teaching and Learning, 7(6), 836-850. https://doi.org/10.1016/j.cptl.2015.08.001
Hernández, R., & Mendoza, C. (2018). Metodología de la investigación. Las rutas cuantitativa, cualitativa y mixta. Mc Graw Hill Education.
Higgins, J. J. (2004). Introduction to Modern Nonparametric Statistics. Brooks/Cole.
Lobos, K., Bustos, C., Cobo-Rendón, R. C., & Cisternas, N. V. (2022). Características psicométricas de un cuestionario de expectativas hacia la educación virtual en estudiantes universitarios durante la pandemia COVID-19. Formación Universitaria, 15(1), 3-18. https://doi.org/10.4067/S0718-50062022000100003
Martín-Párraga, L., Llorente-Cejudo, C., & Cabero-Almenara, J. (2024). Las TIC como espacio de progreso hacia el Objetivo de Desarrollo Sostenible 4 (ODS4). Revista Lusófona de Educação, 61(61), 75-88. https://doi.org/10.24140/issn.1645-7250.rle61.05
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625-632. https://doi.org/10.1007/s10459-010-9222-y
Pelletier, K., Robert, J., Muscanell, N., McCormack, M., Reeves, J., Arbino, N., Grajek, S., Birdwell, T., Liu, D., Mandernach, J., Moore, A., Porcaro, A., Rutledge, R., & Zimmern, J. (2023). EDUCAUSE Horizon Report, Teaching and Learning Edition. EDUCAUSE. https://library.educause.edu/-/media/files/library/2023/4/2023hrteachinglearning.pdf
Rahmatpour, P., Nazari, R., Sharif-Nia, H., Mohammad Ibrahim, M., Muhammad Ibrahim, F., & Tayebi Myaneh, Z. (2024). The roles of online courses’ design and evaluation in nursing student satisfaction with online learning. E-Learning and Digital Media. https://doi.org/10.1177/20427530241239436
Ramírez García, A. G., Espejel García, A., Pirela Hernández, A. A., & Castillo Escalante, I. C. (2021). Educación virtual: alternativa en un sistema educativo globalizado. Revista Venezolana de Gerencia, 26(6), 376-389. https://doi.org/10.52080/rvgluz.26.e6.23
Remesal, A., & Villarroel, V. (2023). Challenges for Post-Pandemic Virtual Education in Latin America: A Comparative Analysis of the Emergency Remote Higher Education Process in Chile, Mexico, and Ecuador. Sustainability, 15(19), 14199. https://doi.org/10.3390/su151914199
Rivera Tejada, H. S., Otiniano García, N. M., & Goicochea Ríos, E. del S. (2023). Estrategias didácticas de la educación virtual universitaria: Revisión sistemática. Edutec. Revista Electrónica de Tecnología Educativa, (83), 120-134. https://doi.org/10.21556/edutec.2023.83.2683
Roco-Videla, Á., Aguilera-Eguía, R., & Olguin-Barraza, M. (2024). Ventajas del uso del coeficiente de omega de McDonald frente al alfa de Cronbach. Nutrición Hospitalaria, 41(1), 262-263. https://doi.org/10.20960/nh.04879
Senescyt. (2024). Buscador de oferta académica. https://siau.senescyt.gob.ec/buscador-oferta/
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541-542. https://doi.org/10.4300/JGME-5-4-18
Vaca-Cárdenas, M. E., Ordoñez-Ávila, E. R. Vaca-Cárdenas, L. A., & Vaca-Cárdenas, A. N. (2024). Students’ acceptance toward Asynchronous Virtual Education during COVID-19 pandemic. Kasetsart Journal of Social Sciences, 45(2), 483-492. https://doi.org/10.34044/j.kjss.2024.45.2.14
Vásquez Peñafiel, M. S., Nuñez, P., & Cuestas Caza, J. (2023). Competencias digitales docentes en el contexto de COVID-19. Un enfoque cuantitativo. Pixel-Bit, Revista de Medios y Educación, 67(67), 155-185. https://doi.org/10.12795/pixelbit.98129
Wang, W., Peslak, A., Kovacs, P., & Kovalchick, L. (2022). Have students’ perceptions of online education changed with the easing of COVID-19 restrictions? Issues in Information Systems, 23(3), 199-208. https://doi.org/10.48009/3_iis_2022_116
Yan, Y., Vyas, L., Wu, A. M., & Rawat, S. (2022). Effective online education under COVID-19: Perspectives from teachers and students. Journal of Public Affairs Education, 28(4), 422-439. https://doi.org/10.1080/15236803.2022.2110749
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Laura Alexandra Arteaga-Briones, Luis Ángel Guamán-Lazo, Andrea Alexandra Azúa-Arteaga, Alexander López-Padrón
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
