Palabras clave
Aprendizaje, enseñanza presencial, enseñanza online, universidad, innovación educativa, neuroeducación
Resumen
Este trabajo tiene como objetivo registrar y analizar, mediante el uso de neurotecnología, en un contexto formativo universitario presencial y online, el efecto que tiene en variables relevantes en el proceso de aprendizaje, lo cual supone una innovación en la literatura. En este estudio se ha empleado tecnología de neurociencia para medir el procesamiento cognitivo de los estímulos diseñados para una experiencia académica de una clase de máster universitario. Las neurotecnologías empleadas han sido la respuesta galvánica de la piel (GSR), la electroencefalografía (EEG) y el seguimiento ocular. Tras el análisis de los registros cerebrales, basados en la atención, interés, estrés y conexión emocional (engagement), en un contexto educativo presencial y su análisis comparativo con el seguimiento online, los resultados indicaron que los niveles de intensidad emocional de los alumnos que siguieron la clase de forma presencial son más elevados que aquellos que asistieron de forma online. A su vez, los valores de actividad cerebral positiva (atención, interés y engagement) son superiores en el grupo de asistencia presencial, siendo la variable negativa estrés también superior, pudiendo justificarse debido a que los alumnos conectados online no activaban la cámara. Los registros cerebrales de los alumnos que asisten a distancia muestran menor interés y atención, así como una menor intensidad emocional, por lo que el aprendizaje a distancia (online) es menos efectivo, a efectos de señales cerebrales, que la enseñanza en el aula, para una clase teórica de máster universitario.
Referencias
Anand, K., Ruchika, K., Ram, S.K., Iqbal, A., & Puneet, W. (2014). Alternative healing therapies in todays era. International Journal of Research in Ayurveda and Pharmacy, 5(3), 394-396. https://doi.org/10.7897/2277-4343.05381
Link DOI | Link Google Scholar
Ber?ík, J., Horská, E., Gálová, J., & Margianti, E.S. (2016). Consumer neuroscience in practice: The impact of store atmosphere on consumer behavior. Periodica Polytechnica Social and Management Sciences, 24(2), 96-101. https://doi.org/10.3311/PPso.8715
Link DOI | Link Google Scholar
Bernal, I.M. (2022). El examen oral como promotor del aprendizaje activo. Revista Científica Estudios e Investigaciones, 11(1), 130-134. https://doi.org/10.26885/rcei.11.1.130
Link DOI | Link Google Scholar
Bittencourt, T., & Willetts, A. (2018). Negotiating the tensions: A critical study of international schools’ mission statements. Globalisation, Societies and Education, 16(4), 515-525. https://doi.org/10.1080/14767724.2018.1512047
Link DOI | Link Google Scholar
Bowers, J.S. (2016). The practical and principled problems with educational neuroscience. Psychological Review, 123(5), 600. https://doi.org/10.1037/rev0000025
Link DOI | Link Google Scholar
Bueno-i-Torrens, D., & Forés-Miravalles, A. (2018). 5 principles of neuroeducation that families should know to put in practice. Revista Iberoamericana de Educacion, 78(1), 13-25. https://doi.org/10.35362/rie7813255
Link DOI | Link Google Scholar
Bueno-i-Torrens, D., & Forés-Miravalles, A. (2021). Neuroscience applied to education: How the brain learns and what consequences this has. Llengua Societat I Comunicació, 19, 37-45. https://doi.org/10.1344/LSC-2021.19.5
Link DOI | Link Google Scholar
Campbell, S.R. (2011). Educational Neuroscience: Motivations, methodology, and implications. Educational Philosophy and Theory, 43(1), 7-16. https://doi.org/10.1111/j.1469-5812.2010.00701.x
Link DOI | Link Google Scholar
Carew, T.J., & Magsamen, S.H. (2010). Neuroscience and education: An ideal partnership for producing evidence-based solutions to guide 21st century learning. Neuron, 67(5), 685-688. https://doi.org/10.1016/j.neuron.2010.08.028
Link DOI | Link Google Scholar
Chávez-Miyauchi, T.E., Benitez-Rico, A., Alcántara-Flores, M., Vergara-Castañeda, A., & Ogando-Justo, A.B. (2021). Personal motivation and learning self-management in students, as result of the transition to online courses during COVID-19 pandemic. Nova scientia, 13. https://doi.org/10.21640/ns.v13ie.2739
Link DOI | Link Google Scholar
Clark, D.B. Tanner-Smith, E.E., & Killingsworth, S.S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86(1), 79-122. https://doi.org/10.3102/0034654315582065
Link DOI | Link Google Scholar
Cuesta-Cambra, U., Niño-González, J., & Rodríguez-Terceño, J. (2017). The cognitive processing of an educational app with EEG and ’Eye Tracking’. [El procesamiento cognitivo en una app educativa con electroencefalograma y «Eye Tracking»]. Comunicar, 52, 41-50. https://doi.org/10.3916/C52-2017-04
Link DOI | Link Google Scholar
da-Silva, F.L., Slodkowski, B.K., da Silva, K.K.A., & Cazella, S.C. (2023). A systematic literature review on educational recommender systems for teaching and learning: Research trends, limitations and opportunities. Education and Information Technologies, 28, 3289-3328. https://doi.org/10.1007/s10639-022-11341-9
Link DOI | Link Google Scholar
Doyle, A., Seery, N., Canty, D., & Buckley, J. (2019). Agendas, influences, and capability: Perspectives on practice in design and technology education. International Journal of Technology and Design Education, 29(1), 143-159. https://doi.org/10.1007/s10798-017-9433-0
Link DOI | Link Google Scholar
Duchowski, A.T. (2007). Eye tracking techniques. In A.T. Duchowski (Ed.), Eye tracking methodology (pp. 51-59). Springer. https://doi.org/10.1007/978-3-319-57883-5
Link DOI | Link Google Scholar
Ferrari, M. (2011). What can neuroscience bring to education? Educational Philosophy and Theory, 43(1), 31-36. https://doi.org/10.1111/j.1469-5812.2010.00704.x
Link DOI | Link Google Scholar
Ghergulescu, I., & Hava-Muntean, C. (2016). ToTCompute: A novel EEG-based TimeOnTask threshold computation mechanism for engagement modelling and monitoring. International Journal of Artificial Intelligence in Education, 26(3), 821-854. https://doi.org/10.1007/s40593-016-0111-2
Link DOI | Link Google Scholar
Hillman, T. (2011). The inscription, translation and re-inscription of technology for mathematical learning. Technology, Knowledge and Learning, 16, 103-124. https://doi.org/10.1007/s10758-011-9182-1
Link DOI | Link Google Scholar
Horn, C., Snyder, B. P., Coverdale, J. H., Louie, A. K., & Roberts, L. W. (2009). Educational Research Questions and Study Design. Academic Psychiatry, 33, 261-267. https://doi.org/10.1176/appi.ap.33.3.261
Link DOI | Link Google Scholar
Howard-Jones, P.A. (2014). Neuroscience and education: Myths and messages. Nature Reviews Neuroscience, 15, 817-824. https://doi.org/10.1038/nrn3817
Link DOI | Link Google Scholar
Huamán-Romaní, Y.L., Estrada-Pantía, J. L., Olivares-Rivera, O., Rodas-Guizado, E., & Fuentes-Bernedo, F. E. (2021). Use of technological equipment for e-learning in Peruvian university students in times of Covid-19. International Journal of Emerging Technologies in Learning, 16(20), 119-133. https://doi.org/0.3991/ijet.v16i20.24661
Link DOI | Link Google Scholar
Isen, A.M., & Reeve, J. (2005). The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation and Emotion, 29(4), 295-323. https://doi.org/10.1007/s11031-006-9019-8
Link DOI | Link Google Scholar
Juarez, D., Tur-Viñes, V., & Mengual, A. (2020). Neuromarketing Applied to Educational Toy Packaging. Frontiers in Psychology, 11, 2077. https://doi.org/10.3389/fpsyg.2020.02077
Link DOI | Link Google Scholar
Klingner, J. K., & Boardman, A.G. (2011). Addressing the “research gap” in special education through mixed methods. Learning Disability Quarterly, 34(3), 208-218. https://doi.org/10.1177/0731948711417559
Link DOI | Link Google Scholar
Lai, J.W.M., & Bower, M. (2019). How is the use of technology in education evaluated? A systematic review. Computers & Education, 133, 27-42. https://doi.org/10.1016/j.compedu.2019.01.010
Link DOI | Link Google Scholar
Lee, C., Yeung, A.S., & Cheung, K.W. (2019). Learner perceptions versus technology usage: A study of adolescent English learners in Hong Kong secondary schools. Computers & Education, 133, 13-26. https://doi.org/10.1016/j.compedu.2019.01.005
Link DOI | Link Google Scholar
Lin, J.S., & Hsieh, C.H. (2016). A wireless BCI-controlled integration system in smart living space for patients. Wireless Personal Communications, 88(2), 395-412. https://doi.org/10.1007/s11277-015-3129-0
Link DOI | Link Google Scholar
Morgado-Bernal, I. (2005). The psychobiology of learning and memory fundamentals and recent advances [Review]. Revista De Neurologia, 40(5), 289-297. https://doi.org/10.33588/rn.4005.2005004
Link DOI | Link Google Scholar
Morgan, J. (2015). Online Versus face-to-face accounting education: A comparison of CPA exam outcomes across matched institutions. Journal of Education for Business, 90(8), 420-426. https://doi.org/10.1080/08832323.2015.1087371
Link DOI | Link Google Scholar
Plassmann, H., Zoëga-Ramsøy, T., & Milosavljevic, M.(2012). Branding the brain: A critical review and outlook. Journal of Consumer Psychology, 22(1), 18-36. https://doi.org/10.1016/j.jcps.2011.11.010
Link DOI | Link Google Scholar
Price, L., Richardson, J.T.E., & Jelfs, A. (2007). Face-to-face versus online tutoring support in distance education. Studies in Higher Education, 32(1), 1-20. https://doi.org/10.1080/03075070601004366
Link DOI | Link Google Scholar
Prinzel, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., & Pope, A.T. (2009). A closed-loop system for examining psychophysiological measures for adaptive task allocation. The International journal of aviation psychology, 10(4), 393-410. https://doi.org/10.1207/S15327108IJAP1004_6
Link DOI | Link Google Scholar
Ramele, R., Villar, A.J., & Santos, J.M. (2012). EPOC Emotiv EEG Basics. https://bit.ly/3FrQKPH
Link Google Scholar
Ramírez-Montoya, M., & Lugo-Ocando, J. (2020). Systematic review of mixed methods in the framework of educational innovation. [Revisión sistemática de métodos mixtos en el marco de la innovación educativa]. Comunicar, 65, 9-20. https://doi.org/10.3916/C65-2020-01
Link DOI | Link Google Scholar
Rikkerink, M., Verbeeten, H., Simons, R.S., & Ritzen, H. (2016). A new model of educational innovation: Exploring the nexus of organizational learning, distributed leadership, and digital technologies. Journal of Educational Change, 17, 223-249. https://doi.org/10.1007/s10833-015-9253-5
Link DOI | Link Google Scholar
Saeed, S., & Zyngier, D. (2012). How motivation influences student engagement: A qualitative case study. Journal of Education and Learning, 1(2), 252-267. https://doi.org/10.5539/jel.v1n2p252
Link DOI | Link Google Scholar
Sánchez-Mendiola, M., Martínez-Hernández, A.M.P., Torres-Carrasco., Agüero-Servín, M.M., Hernández-Romo, A.K., Benavides-Lara, M.A., Rendón-Cazales, V.J., & Jaímes-Vergara, C.A. (2020). Retos educativos durante la pandemia de COVID-19: Una encuesta a profesores de la UNAM. Revista digital universitaria, 21(3), 1-24. https://doi.org/10.22201/codeic.16076079e.2020.v21n3.a12
Link DOI | Link Google Scholar
Serrano-Díaz, N., Aragón-Mendizábal, E., & Mérida-Serrano, R. (2022). Families’ perception of children’s academic performance during the COVID-19 lockdown. [Percepción de las familias sobre el desempeño escolar durante el confinamiento por COVID-19]. Comunicar, 70, 59-68. https://doi.org/10.3916/C70-2022-05
Link DOI | Link Google Scholar
Sevimli, E. (2022). Evaluation of the didactic transposition process in teaching integral: Face-to-Face versus online education. International Journal for Technology in Mathematics Education, 29(1), 37-48. https://doi.org/10.1564/tme_v29.1.04
Link DOI | Link Google Scholar
Talmi, D., Anderson, A., Riggs, L., Caplan, J.B., & Moscovitch, M. (2008). Immediate memory consequences of the effect of emotion on attention to pictures. Learning & Memory, 15(3), 172-182. https://doi.org/10.1101/lm.722908
Link DOI | Link Google Scholar
Thomas, M.S.C., Ansari, D., & Knowland, V.C.P. (2019). Annual Research Review: Educational neuroscience: progress and prospects [Review]. Journal of Child Psychology and Psychiatry, 60(4), 477-492. https://doi.org/10.1111/jcpp.12973
Link DOI | Link Google Scholar
Torras, M., Portell, I., & Morgado-Bernal, I. (2001). The amygdaloid body: Functional implications. Revista de Neurologia, 33(5), 471-476. https://doi.org/10.33588/rn.3305.2001125
Link DOI | Link Google Scholar
Van-Ameringen, M., Mancini, C., & Favorlden, P. (2003). The impact of anxiety disorders on educational achievement. Journal of Anxiety disorders, 17(5), 561-571. https://doi.org/10.1016/S0887-6185(02)00228-1
Link DOI | Link Google Scholar
Van-Doorn, J., Lemon, K.N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P.C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253-266. https://doi.org/10.1177/1094670510375599
Link DOI | Link Google Scholar
Varma, S., McCandliss, B.D., & Schwartz, D.L. (2008). Scientific and pragmatic challenges for bridging education and neuroscience. Educational Researcher, 37(3), 140-152. https://doi.org/10.3102/0013189X08317687
Link DOI | Link Google Scholar
Villardón-Gallego, L., García-Carrión, R., Tánez-Marquina., & Estévez, A. (2018). Impact of the interactive learning environments in children’s prosocial behavior. Sustainability, 10(7), 2138. https://doi.org/10.3390/su10072138
Link DOI | Link Google Scholar
Wang, C.C., & Hsu, M.C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912-923. https://doi.org/10.1016/j.im.2014.05.010
Link DOI | Link Google Scholar
Waxman, H.C., Wirr-Boriack, A., Lee, Y.H., & MacNeil, A. (2013). Principals' perceptions of the importance of technology in schools. Contemporary Educational Technology, 4(3), 187-196. https://doi.org/10.30935/cedtech/6102
Link DOI | Link Google Scholar
Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human Behavior, 81, 340-349. https://doi.org/10.1016/j.chb.2017.12.037
Link DOI | Link Google Scholar
Yadava, M., Kumar, P., Saini, R., Pratim-Roy, P., & Prosad-Dogra, D. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 76(18), 19087-19111. https://doi.org/10.1007/s11042-017-4580-6
Link DOI | Link Google Scholar
Fundref
Este trabajo no tiene ningún soporte financiero
Ficha técnica
Recibido: 25-12-2022
Revisado: 22-01-2023
Aceptado: 23-02-2023
OnlineFirst: 30-05-2023
Fecha publicación: 01-07-2023
Tiempo de revisión del artículo : 28 (en días) | Media de tiempo de revisión de los manuscritos del número 76: -6 (en días)
Tiempo de aceptación del artículo: 60 (en días) | Media tiempo aceptación de los manuscritos del número 76: 72 (en días)
Tiempo de edición OnlineFirst: 142 (en días) | Media tiempo edición de los OnlineFirst del número 76: 154 (en días)
Tiempo de publicacicón final del artículo: 187 (en días) | Media tiempo de publicación final de los articulos del número 76: 199 (en días)
Métricas
Métricas de este artículo
Vistas: 30051
Lectura del abstract: 28962
Descargas del PDF: 1089
Métricas completas de Comunicar 76
Vistas: 374375
Lectura del abstract: 365779
Descargas del PDF: 8596
Citado por
Citas en Web of Science
Actualmente no existen citas hacia este documento
Citas en Scopus
Actualmente no existen citas hacia este documento
Citas en Google Scholar
MusREL: A Utility-Weighted Multi-Strategy Relation Extraction Model-Based Intelligent System for Online Education Z Zhu, H Lin, D Gu, L Wang, H Wu… - International Journal on …, 2023 - igi-global.com
https://www.igi-global.com/article/musrel/329965
1
