Analytics of Learning and Educational Neurosciences: challenges in technological integration
DOI:
https://doi.org/10.35362/rie8013428Keywords:
learning analytics, neuroscience, educational challengesAbstract
The aim of learning analytics (LA) is to understand and optimize learning and the environments in which it occurs. Given the complex nature of learning, it has become necessary to use tools from various fields of research to obtain, describe, analyze and interpret data about students, their learning processes and contexts. Rather than taking isolated methods or techniques for the study of this process, learning analytics is beginning to integrate the perspectives of other fields to achieve a research of learning with a transdisciplinary vision. One of these fields is neurosciences, specifically those related to learning or educational neurosciences. The objective of this research is to explore the implications and challenges of the use of EEG technologies (traditionally used in neurological studies) in conjunction with learning analytics for the study of students learning processes and their context. This research focus on the study of the brain waves of a single sample of 6 students during a class session. This standpoint, framed within the educational neurosciences, constitutes a tool of analysis that can be used to achieve a quality teaching.
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References
Alberca, E. y Borrueco, M, (2018). Aprendizaje: el cerebro en el aula. Ciencia e innovación docente en el aprendizaje de lenguas extranjeras. Ediciones Egregius.
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
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
How to Cite
Corona Ferreira, A., Altamirano, M., López Ortega, M. de los Ángeles, & González González, O. A. (2019). Analytics of Learning and Educational Neurosciences: challenges in technological integration. Iberoamerican Journal of Education, 80(1), 31–54. https://doi.org/10.35362/rie8013428
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Published
2019-05-14
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Monographic. Learning Analytics and Education
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