Analítica da Aprendizagem e a neurociência educacional: novos desafios na integração tecnológica
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https://doi.org/10.35362/rie8013428Palavras-chave:
Analítica de Aprendizagem, neurociência, desafios educacionaisResumo
A Analítica da Aprendizagem (LA) tem como objetivo compreender e otimizar a aprendizagem e os ambientes nos quais ela ocorre. Dada a complexa natureza do processo de aprendizagem, tornou-se necessário o uso de ferramentas de diversos campos de pesquisa para a obtenção, descrição, análise e interpretação de dados sobre os alunos durante seus processos de aprendizagem e em seus contextos. Ao invés de usar métodos ou técnicas isoladas para o estudo deste processo, a Analítica da Aprendizagem está começando a integrar as perspectivas de outros campos para alcançar uma pesquisa de aprendizagem com uma visão transdisciplinar. Um destes campos é o da neurociência, especificamente aquela relativa à aprendizagem ou neurociência educacional. O objetivo desta pesquisa é explorar as implicações e os desafios do uso das tecnologias por meio do eletroencefalograma (EEG), tradicionalmente aplicado em estudos neurológicos, em conjunto com as Analíticas de Aprendizagem para o estudo dos processos de aprendizagem nos estudantes.
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Referências
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Blikstein, P. y Worsley, M. (2016). Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks. Journal of Learning Analytics, 3(2), 220-238. https://doi.org/10.18608/jla.2016.32.11
Corrin, L., Alhadad, S., Jones, H. y Colvin, C. (2018). Where is the field of learning analytics heading? Reflections from the Learning Analytics & Knowledge Conference. Recuperado de: https://bit.ly/2V3pkKR.
Das, R., Chatterjee, D., Sinharay, A. y Sinha, A. (2014). Cognitive Load Measurement - A Methodology to Compare Low Cost Commercial EEG Devices. Advances in Computing, Communications and Informatics, ICACCI International Conference. IEEE pp.1188–1194.
Di Mitri, D., Schneider, J., Specht, M., y Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349. https://doi.org/10.1111/jcal.12288
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Durall, E., Leinonen, T., Gros, B., y Rodriguez-Kaarto, T. (2017). Reflection in Learning through a Self-monitoring Device: Design Research on EEG Self-Monitoring during a Study Session. Designs for Learning, 9(1), 10–20. https://doi.org/10.16993/dfl.75
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Henríquez, C (2014). Memoria de Trabajo de Fin de Máster Estudio de Técnicas de análisis y clasificación de señales EEG en el contexto de Sistemas BCI (Brain Computer Interface). Master Universitario en Investigación e Innovación en TIC. Universidad Autónoma de Madrid. Escuela Politécnica Superior. Departamento de Ingeniería Informática, pp 17-19.
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Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A., y D’Mello, S.K. (2017). Put your thinking cap on: detecting cognitive load using EEG during learning. LAK. 80-89
Nigay, L., y Coutaz, J. (1993). A design space for multimodal systems: Concurrent processing and data fusion. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems –CHI 93, (January 1993), 172–178. http://doi.org/10.1145/169059.169143
Ninaus, M., Kober, S. E., Friedrich, E. V., Neuper, C. y Wood, G. (2014). The Potential Use of Neurophysiological Signals for Learning Analytics. 2014 6th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES). Valleta: Malta. https://doi.org/10.1109/vs-games.2014.7012169
Ramos-Galarza, C., Paredes, L., Andrade, S., Santillán, W. y González,L (2017). Sistemas de Atención Focalizada, Sostenida y Selectiva en Universitarios de Quito-Ecuador. Revista Ecuatoriana de Neurologia 25(1-3):34-38
Reimann, P. (2016). Connecting learning analytics with learning research: the role of design-based research. Learning: Research and Practice, 2:2, 130-142. https://doi.org/10.1080/23735082.2016.1210198
Rogers, J. M., Johnstone, S. J., Aminov, A., Donnelly, J. y Wilson, P. H. (2016). Test-retest reliability of a single-channel, wireless EEG system. International Journal of Psychophysiology, 106, 87-96.
Romero, C., Luna, J.M., Bogarín, A., Luque, M., y Gonzalez. P. (2018) Análisis del nivel de atención de los alumnos en clase utilizando Neurosky’s mindwave mobile. Revista de Innovación y Buenas Prácticas Docentes. 5, 57-62 Recuperado de: https://bit.ly/2Wckc3r
Siemens, G. (2013). Learning analytics: The emergences of a discipline. American Behavior Scientist. 57(10) 1380-140.
Siemens, G. (04, marzo, 2016) Neuroscience and Learning Analytics: a historic leap in understanding learning. [audio en Podcast]. Recuperado de: https://bit.ly/2IQAHiY
Society for Learning Analytics Research (SoLAR). (s.f.) Recuperado de https://bit.ly/2DDFtMc
Stewart, C. (2017). Learning Analytics: Shifting from theory to practice. Journal on Empowering Teaching Excellence, 1(1), 95-105. https://doi.org/10.15142/T3G63W
Tseng, S., Yu, L. y Wu, H. (2014). Measuring Engagement: Student Profiling and the Effects of Remedial Learning Counseling. International Workshop on Learning Analytics and Educational Data Mining (LAEDM 2016) In conjunction with CRIWG/CollabTech, 14-17.
Zadina, J. (2015) The emerging role of educational neuroscience in education reform. Psicología Educativa, 21, 71-77. Recuperado de https://bit.ly/2PC9DUY
Amo, D. y Santiago, R. (2017) Learning Analytics. La narración del aprendizaje a través de los datos. Barcelona: Editorial UOC.
Ansari, D. , Coch, D. y De Smedt, B. (2011). Conexión entre la educación y la neurociencia cognitiva: ¿hacia dónde nos llevará el viaje? Filosofía y teoría educativa 43(1), 37-42. https://doi.org/10.1111/j.1469-5812.2010.00705.x
Banihashem S. K., Aliabadi, K., Pourroostae, S., Delaver A, Nili Ahmadabadi, M. (2018). Learning Analytics: A Systematic Literature Review, Interdisciplinary Journal of Virtual Learning in Medical Science. 9(2). https://doi.org/10.5812/ijvlms.63024.
Blikstein, P. y Worsley, M. (2016). Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks. Journal of Learning Analytics, 3(2), 220-238. https://doi.org/10.18608/jla.2016.32.11
Corrin, L., Alhadad, S., Jones, H. y Colvin, C. (2018). Where is the field of learning analytics heading? Reflections from the Learning Analytics & Knowledge Conference. Recuperado de: https://bit.ly/2V3pkKR.
Das, R., Chatterjee, D., Sinharay, A. y Sinha, A. (2014). Cognitive Load Measurement - A Methodology to Compare Low Cost Commercial EEG Devices. Advances in Computing, Communications and Informatics, ICACCI International Conference. IEEE pp.1188–1194.
Di Mitri, D., Schneider, J., Specht, M., y Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349. https://doi.org/10.1111/jcal.12288
Díaz, C., Martínez, P., Roa, I y Sanhueza, M.G. (2010). Los docentes en la sociedad actual: sus creencias y cogniciones pedagógicas respecto al proceso didáctico, Polis. Revista Latinoamericana, 25.
Durall, E., Leinonen, T., Gros, B., y Rodriguez-Kaarto, T. (2017). Reflection in Learning through a Self-monitoring Device: Design Research on EEG Self-Monitoring during a Study Session. Designs for Learning, 9(1), 10–20. https://doi.org/10.16993/dfl.75
Falconi, A., Alajo, A., Cueva, M., Mendoza, R. Ramírez, S. y Palma, E. (2017). Las neurociencias. Una visión de su aplicación en la educación. Revista Órbita Pedagógica 4(1), 61-74
Ferguson, R. y Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In: LAK ’17 Proceedings of the Seventh International Learning Analytics & Knowledge Conference, ACM International Conference Proceeding Series, pp. 56-65. New York, USA.
Henríquez, C (2014). Memoria de Trabajo de Fin de Máster Estudio de Técnicas de análisis y clasificación de señales EEG en el contexto de Sistemas BCI (Brain Computer Interface). Master Universitario en Investigación e Innovación en TIC. Universidad Autónoma de Madrid. Escuela Politécnica Superior. Departamento de Ingeniería Informática, pp 17-19.
Immordino-Yang, M. H. y Damasio, A. (2008). We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education. Mind, Brain, and Education, 1(1), 3-10. https://doi.org/10.1111/j.1751-228x.2007.00004.x
Linarez, G. (2016). Aprendizaje significativo y neurociencia: la conexión del siglo XXI. Revista Iberoamericana de Producción Académica y Gestión Educativa, 4. 116-141
Lodge, J. M., y Corrin, L. (2017). What data and analytics can and do say about effective learning. Npj Science of Learning, 2(1). https://doi.org/10.1038/s41539-017-0006-5
Lodge, J., Hoovarth, J. y Corrin, L. (ed.) (2018) Learning Analytics in the Classroom: Translating Learning Analytics Research for Teachers. (1st Edition). London: Taylor and Francis. https://doi.org/10.4324/9781351113038
Mandinach, E.B. ( 2012 ). Un momento perfecto para el uso de datos: usar la toma de decisiones basada en datos para informar la práctica . Psicólogo de la educación , 47 (2), 71 - 85
Mellender, F. (2015). NeuroExperimenter. Fred Mellender’s Home Page. Recuperado de https://bit.ly/2CuC9li
Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A., y D’Mello, S.K. (2017). Put your thinking cap on: detecting cognitive load using EEG during learning. LAK. 80-89
Nigay, L., y Coutaz, J. (1993). A design space for multimodal systems: Concurrent processing and data fusion. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems –CHI 93, (January 1993), 172–178. http://doi.org/10.1145/169059.169143
Ninaus, M., Kober, S. E., Friedrich, E. V., Neuper, C. y Wood, G. (2014). The Potential Use of Neurophysiological Signals for Learning Analytics. 2014 6th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES). Valleta: Malta. https://doi.org/10.1109/vs-games.2014.7012169
Ramos-Galarza, C., Paredes, L., Andrade, S., Santillán, W. y González,L (2017). Sistemas de Atención Focalizada, Sostenida y Selectiva en Universitarios de Quito-Ecuador. Revista Ecuatoriana de Neurologia 25(1-3):34-38
Reimann, P. (2016). Connecting learning analytics with learning research: the role of design-based research. Learning: Research and Practice, 2:2, 130-142. https://doi.org/10.1080/23735082.2016.1210198
Rogers, J. M., Johnstone, S. J., Aminov, A., Donnelly, J. y Wilson, P. H. (2016). Test-retest reliability of a single-channel, wireless EEG system. International Journal of Psychophysiology, 106, 87-96.
Romero, C., Luna, J.M., Bogarín, A., Luque, M., y Gonzalez. P. (2018) Análisis del nivel de atención de los alumnos en clase utilizando Neurosky’s mindwave mobile. Revista de Innovación y Buenas Prácticas Docentes. 5, 57-62 Recuperado de: https://bit.ly/2Wckc3r
Siemens, G. (2013). Learning analytics: The emergences of a discipline. American Behavior Scientist. 57(10) 1380-140.
Siemens, G. (04, marzo, 2016) Neuroscience and Learning Analytics: a historic leap in understanding learning. [audio en Podcast]. Recuperado de: https://bit.ly/2IQAHiY
Society for Learning Analytics Research (SoLAR). (s.f.) Recuperado de https://bit.ly/2DDFtMc
Stewart, C. (2017). Learning Analytics: Shifting from theory to practice. Journal on Empowering Teaching Excellence, 1(1), 95-105. https://doi.org/10.15142/T3G63W
Tseng, S., Yu, L. y Wu, H. (2014). Measuring Engagement: Student Profiling and the Effects of Remedial Learning Counseling. International Workshop on Learning Analytics and Educational Data Mining (LAEDM 2016) In conjunction with CRIWG/CollabTech, 14-17.
Zadina, J. (2015) The emerging role of educational neuroscience in education reform. Psicología Educativa, 21, 71-77. Recuperado de https://bit.ly/2PC9DUY
Como Citar
Corona Ferreira, A., Altamirano, M., López Ortega, M. de los Ángeles, & González González, O. A. (2019). Analítica da Aprendizagem e a neurociência educacional: novos desafios na integração tecnológica. Revista Ibero-Americana De Educação, 80(1), 31–54. https://doi.org/10.35362/rie8013428
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Publicado
2019-05-14
Edição
Secção
Monográfico. Learning Analytics and education
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