Métricas alternativas
Palabras clave
inteligencia artificial
Enseñanza y Aprendizaje
Educación superior
Tecnologías de la Información y la Comunicación Blended learning
Artificial intelegence
ICT and teaching
teaching and learning
higher education
Cómo citar
Resumen
Este estudio investiga los factores que influyen en la implementación del aprendizaje combinado y la inteligencia artificial (IA) en la enseñanza y el aprendizaje en Indonesia. Se proponen cinco factores: Estructura y Sistema de Apoyo, Horario de Aprendizaje Combinado, Apoyo Pedagógico, Método en el Aprendizaje Combinado y Evaluación en el Aprendizaje Combinado. El estudio empleó un método de encuesta por cuestionario para recopilar datos de 625 profesores que habían implementado el aprendizaje combinado en universidades de Indonesia. Para el análisis de los datos se empleó el modelo de ecuaciones estructurales mediante el método de mínimos cuadrados parciales (PLS-SEM). Los resultados del estudio demuestran que las cinco variables independientes (Estructura y Sistema de Apoyo, Horario de Aprendizaje Combinado, Apoyo Pedagógico, Método en Aprendizaje Combinado y Evaluación en BL) exhiben una correlación positiva significativa con la variable dependiente, Blended Learning-IA en la enseñanza del aprendizaje. Los resultados de este estudio aportan evidencia empírica para apoyar las hipótesis propuestas. El estudio también proporciona información sobre los factores que pueden influir en la eficacia de la implementación del aprendizaje combinado y la IA en la enseñanza y el aprendizaje.
Citas
Arul Raj, D. A. A. R., Rukmani, K. V. R., Rukmani, K. C., Rukmani, P. M., & Rukmani, A. A. (2024). Impact of artificial intelligence in language learning—A survey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(2), 258–266. https://doi.org/10.32628/CSEIT2410218
Camacho-Zuñiga, C., Peña-Becerril, M., Cuevas-Cancino, M. D. L. O., & Avilés-Rabanales, E. G. (2023). Gains from the transition from face-to-face to digital/online modality to improve education in emergency. Frontiers in Education, 8, 1197396. https://doi.org/10.3389/feduc.2023.1197396
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1), 29. https://doi.org/10.1186/s40594-023-00418-7
Davison, E., & Price, J. (2009). How can interactive multimedia change the way we create, teach, and learn? The Western Journal of Emergency Medicine, 10(1), 47–52.
Delenko, V., & Sholovii, M.-T. (2023). Blended learning as a way to the regeneration of modern education. Visnyk of the Lviv University. Series Pedagogics, 39, 51–58. https://doi.org/10.30970/vpe.2023.39.12033
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 12. https://doi.org/10.1186/s40561-023-00231-3
Goyal, S., Khaliq, F., & Vaney, N. (2023). Implementation of the online learning management system ‘Moodle’ as a blended approach to online teaching. Indian Journal of Physiology and Pharmacology, 67, 64–72. https://doi.org/10.25259/IJPP_208_2022
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. Internet and Higher Education, 18, 4–14. https://doi.org/10.1016/j.iheduc.2012.09.003
Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hakim, M. A. R., Serasi, R., Revola, Y., Adnan, N. I., & Nur Astari, A. R. (2023). The reconstruction of blended learning teaching model for full online learning system: A study on English Education Department in Islamic Universities in Indonesia. International Journal of Learning, Teaching and Educational Research, 22(4), 1–20. https://doi.org/10.26803/ijlter.22.4.1
Henseruk, H., Tereshchuk, H., Sysoiev, O., & Vasylenko, O. (2023). Blended learning in the context of digital transformation of higher education. The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University. Series: Pedagogy, 1(1), 35–45. https://doi.org/10.25128/2415-3605.23.1.5
Hernández, R. M., Ugaz, W. A. C., Tarrillo, S. J. S., Vasquez, S. J. A., Ordoñez, S. E. L., Montenegro, R. A., Martínez, D. E. E., & Guillen, D. E. F. (2024). Exploring software infrastructures for enhanced learning environments to empowering education. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 15(1), 231–243. https://doi.org/10.58346/JOWUA.2024.I1.016
Henritius, E., Löfström, E., & Hannula, M. S. (2019). The role of pedagogical support in online learning and its impact on student satisfaction. Education and Information Technologies, 24(5), 3021–3039. https://doi.org/10.1007/s10639-019-09915-6
Hoffman, B., & Lowe, D. (2011). The effects of early scaffolding and structured assignment on the academic performance of college students in online learning environments. The Internet and Higher Education, 14(3), 179–187. https://doi.org/10.1016/j.iheduc.2011.04.001
Inprasitha, M. (2023). Blended learning classroom model: A new extended teaching approach for new normal. International Journal for Lesson & Learning Studies, 12(4), 288–300. https://doi.org/10.1108/IJLLS-01-2023-0011
Kebir, B. (2023). The importance of teacher education in coping with the challenges of the digitalization in Germany. Proceedings of The World Conference on Research in Teaching and Education, 1(1), 22–34. https://doi.org/10.33422/worldte.v1i1.10
Li, K. C., & Wong, B. T.-M. (2023). Artificial intelligence in personalised learning: A bibliometric analysis. Interactive Technology and Smart Education, 20(3), 422–445. https://doi.org/10.1108/ITSE-01-2023-0007
McGuinness, C., & Fulton, C. (2019). Digital literacies in higher education: Exploring textual and technological online learning environments. Research in Learning Technology, 27. https://doi.org/10.25304/rlt.v27.2110
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
Panova, E., Tiumentseva, E., Jumanova, Z., Dustmamatova, N., Gorokhova, A., & Sekerin, V. (2023). Distance Learning in Teaching the Russian Language (the Experience of Tashkent State University). Journal of Language Teaching and Research, 14(2), 278–285. https://doi.org/10.17507/jltr.1402.02
Park, Y., & Doo, M. Y. (2024). Role of AI in Blended Learning: A Systematic Literature Review. The International Review of Research in Open and Distributed Learning, 25(1), 164–196. https://doi.org/10.19173/irrodl.v25i1.7566
Putra, A. B. R. N., Mukhadis, A., Soraya, D. U., & Dzakiya, N. (2019). Enhancing learning outcomes through blended learning: A challenge for higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–12. https://doi.org/10.1186/s41239-019-0176-8
Rahmi, U., Ramadhani Fajri, B., & Azrul, A. (2024). Effectiveness of Interactive Content with H5P for Moodle-Learning Management System in Blended Learning. Journal of Learning for Development, 11(1), 66–81. https://doi.org/10.56059/jl4d.v11i1.1135
Sallam, M., Salim, N., Barakat, M., & Al-Tammemi, A. (2023). ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations. Narra J, 3(1), e103. https://doi.org/10.52225/narra.v3i1.103
Southern Federal University, & Moskvin, K. M. (2023). Blended learning as a model in the effective profiling of students in the system of secondary general education. Research Result Pedagogy and Psychology Of Education, 9(2). https://doi.org/10.18413/2313-8971-2023-9-2-0-2
Stone, C. (2023). Artificial intelligence in social work practice education. The potential use of Generative AI for learning. The Journal of Practice Teaching and Learning, 20(3). https://doi.org/10.1921/jpts.v20i3.2192
the School of Education, Faculty of Social Sciences and Humanities, University of Technology Malaysia, Johor Bahru, Malaysia, Yajie, C., & Jumaat, N. F. B. (2023). Blended Learning Design of English Language Course in Higher Education: A Systematic Review. International Journal of Information and Education Technology, 13(2), 364–372. https://doi.org/10.18178/ijiet.2023.13.2.1815
Tuğteki̇N, U. (2023). Factors influencing online learning fatigue among blended learners in higher education. Journal of Educational Technology and Online Learning, 6(1), 16–32. https://doi.org/10.31681/jetol.1161386
Walker, J. D., Brooks, D. C., Hammond, K., Fall, B. A., Peifer, R. W., Schnell, R., & Schottel, J. L. (2014). The use of quiz apps to enhance student learning in introductory biology. Journal of Microbiology & Biology Education, 15(1), 45–47. https://doi.org/10.1128/jmbe.v15i1.701
Wan, M. (2023). A case study of EFL blended learning based on Superstar Learning System in China. LLT Journal: A Journal on Language and Language Teaching, 26(1), 290–303. https://doi.org/10.24071/llt.v26i1.5794
Warner, S., Mohammed, J., & Gross-La Borde, M. (2023). From traditional to home-based learning: Challenges of elementary school teachers during COVID-19 in Trinidad and Tobago. International Journal of Innovation in Teaching and Learning (IJITL), 8(2), 1–19. https://doi.org/10.35993/ijitl.v8i2.1506
Xiao, J., & Jiang, Z. (2023). An M-Learning Model in the Context of the Blended Synchronous Learning Environment: A Pilot Study. International Journal of Mobile and Blended Learning, 15(2), 1–16. https://doi.org/10.4018/IJMBL.318243
Yc, N., Raj, J. B., Nithiya, D. R., Adkoli, B., & Basavarajegowda, A. (2023). Self-reflection and perception of medical and allied courses faculty to online teaching compared to traditional teaching: A mixed-method study. Indian Journal of Physiology and Pharmacology, 67, 226–233. https://doi.org/10.25259/IJPP_146_2022
Zhang, L., Guo, W., Lv, C., Guo, M., Yang, M., Fu, Q., & Liu, X. (2024). Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress. Animal Research and One Health, 2(1), 93–109. https://doi.org/10.1002/aro2.44
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Derechos de autor 2024 Pixel-Bit. Revista de Medios y Educación