Adaptación tecnológica de los estudiantes a docentes de educación inicial: un modelo de ecuaciones estructurales
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https://doi.org/10.31637/epsir-2025-2021Palabras clave:
adaptación tecnológica, formación tecnológica, competencias digitales, actitud hacia la tecnología, docentes de educación inicial, ecuaciones estructurales, estudiantesResumen
Introducción: Este estudio analizó los factores que influyen en la adaptación tecnológica de estudiantes y docentes de educación inicial, mediante un modelo de ecuaciones estructurales. Metodología: Se utilizó un enfoque cuantitativo, con un diseño no experimental, transversal y correlacional, aplicando un cuestionario validado a 546 estudiantes de seis universidades de Ecuador. Resultados: La Formación Tecnológica (β = 0.375) y el Apoyo Institucional (β = 0.305) influyen significativamente en las Actitudes hacia la Tecnología, que afectan directamente las Competencias Digitales (β = 0.508) y los Resultados de Aprendizaje (β = 0.556). Conclusiones: El estudio subraya la importancia de fortalecer programas de formación tecnológica e institucional para integrar eficazmente las TIC en la educación inicial.
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Akabayashi, H., Taguchi, S. y Zvedelikova, M. (2024). School ICT resources, teachers, and online education: Evidence from school closures in Japan during the COVID-19 pandemic. Education Economics, 1-14. https://doi.org/10.1080/09645292.2024.2362917 DOI: https://doi.org/10.1080/09645292.2024.2362917
Akai, J., Osei, R., Adjei, R. y Adiza, P. (2024). Analyzing the challenges basic school teachers face in integrating Information and Communication Technology into teaching and learning activities in a developing country. Cogent Education, 11(1), 2364544. https://doi.org/10.1080/2331186X.2024.2364544 DOI: https://doi.org/10.1080/2331186X.2024.2364544
Berşe, S., Akça, K., Dirgar, E. y Kaplan Serin, E. (2024). The role and potential contributions of the Artificial Intelligence Language Model ChatGPT. Annals of Biomedical Engineering, 52(2), 130-133. https://doi.org/10.1007/s10439-023-03296-w DOI: https://doi.org/10.1007/s10439-023-03296-w
Bower, M., Torrington, J., Lai, J. W. M., Petocz, P. y Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative Artificial Intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12405-0 DOI: https://doi.org/10.1007/s10639-023-12405-0
Bravo, H. M. T., Zambrano, C. K. M., Merchán, V. V. M. y Franco, A. M. (2024). Educación inclusiva, las tic, tendencias y perspectivas en Ecuador. Conocimiento Global, 9(1), 142-151. https://doi.org/10.70165/cglobal.v9i1.352 DOI: https://doi.org/10.70165/cglobal.v9i1.352
Bukar, U. A., Sayeed, Md. S., Fatimah Abdul Razak, S., Yogarayan, S. y Sneesl, R. (2024). Decision-Making Framework for the Utilization of Generative Artificial Intelligence in Education: A Case Study of ChatGPT. IEEE Access, 12, 95368-95389. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3425172 DOI: https://doi.org/10.1109/ACCESS.2024.3425172
Cabellos, B., Siddiq, F. y Scherer, R. (2024). The moderating role of school facilitating conditions and attitudes towards ICT on teachers’ ICT use and emphasis on developing students’ digital skills. Computers in Human Behavior, 150, 107994. https://doi.org/10.1016/j.chb.2023.107994 DOI: https://doi.org/10.1016/j.chb.2023.107994
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. L. Erlbaum Associates.
Dai, Z., Xiong, J., Zhao, L. y He, X. (2022). The effectiveness of ICT-enhanced teaching mode using activity theory on raising class interaction and satisfaction in an engineering course. Interactive Learning Environments, 1-20. https://doi.org/10.1080/10494820.2022.2086574 DOI: https://doi.org/10.1080/10494820.2022.2086574
España, R. M., Muñoz, A., Cantabella, M. y Ayuso, B. (2024). Diseño de acciones formativas para mejorar las competencias digitales del profesorado. Revista Electrónica Interuniversitaria de Formación del Profesorado, 27(1), Article 1. https://doi.org/10.6018/reifop.575071 DOI: https://doi.org/10.6018/reifop.575071
García Ruiz, M. R., Buenestado Fernández, M. y Ramírez Montoya, M. S. (2023). Evaluación de la competencia digital docente: Instrumentos, resultados y propuestas. Revisión sistemática de la literatura. Educación XXI : Revista de la Facultad de Educación. https://doi.org/10.5944/educxx1.33520 DOI: https://doi.org/10.5944/educxx1.33520
Hakimi, M., Shahidzay, A. K., Fazi, A. W. y Qarizada, A. (2024). Empirical Assessment of ICT Impact on Teaching and Learning in High Schools: A Study in the Context of Balkh, Afghanistan. EIKI Journal of Effective Teaching Methods, 2(1). https://journals.eikipub.com/index.php/jetm/article/view/96 DOI: https://doi.org/10.59652/jetm.v2i1.96
Hong, J., Liu, W. y Zhang, Q. (2024). Closing the digital divide: The impact of teachers’ ICT use on student achievement in China. Journal of Comparative Economics, 52(3), 697-713. https://doi.org/10.1016/j.jce.2024.06.003 DOI: https://doi.org/10.1016/j.jce.2024.06.003
König, J., Heine, S., Jäger-Biela, D. y Rothland, M. (2024). ICT integration in teachers’ lesson plans: A scoping review of empirical studies. European Journal of Teacher Education, 47(4), 821-849. https://doi.org/10.1080/02619768.2022.2138323 DOI: https://doi.org/10.1080/02619768.2022.2138323
Maitlo, S. K., Shah, S. A. A. y Ahmed, A. (2024). Use of Information and Communication Technology (ICT). En Teaching English as a Second Language (ESL). Journal of Arts and Linguistics Studies, 2(1), 27-51. https://jals.miard.org/index.php/jals/article/view/84
Ngodu, A., Ndibalema, P. M. y William, F. (2024). Context-relevant strategies for ICT integration in teaching and learning science subjects in Tanzania secondary schools. Educational Technology Quarterly, 1, 20-37. https://doi.org/10.55056/etq.704 DOI: https://doi.org/10.55056/etq.704
Salifu, I., Arthur, F., Arkorful, V., Abam Nortey, S. y Solomon Osei-Yaw, R. (2024). Economics students’ behavioural intention and usage of ChatGPT in higher education: A hybrid structural equation modelling-artificial neural network approach. Cogent Social Sciences, 10(1), 2300177. https://doi.org/10.1080/23311886.2023.2300177 DOI: https://doi.org/10.1080/23311886.2023.2300177
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Derechos de autor 2025 Dayron Rumbaut Rangel, Ana Jacqueline Noblecilla Olaya, Fabrizzio Jacinto Andrade Zamora
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