Neurotecnologias na educação: Avaliação do engajamento, análise da atenção e monitoramento cognitivo dos alunos
DOI:
https://doi.org/10.33448/rsd-v12i13.44422Palavras-chave:
Interfaces cérebro-computador; Ondas encefálicas; Ensino.Resumo
O estudo tem como objetivo explorar o impacto das neurotecnologias na educação, concentrando-se em sua aplicação para avaliar o engajamento, analisar os estados de atenção e monitorar a sobrecarga cognitiva dos alunos. Destaca-se a proliferação de sensores em dispositivos cotidianos para o acompanhamento de parâmetros fisiológicos. A neurotecnologia emerge como uma ferramenta valiosa para capturar insights sobre processos cognitivos, proporcionando métricas relevantes para o engajamento, sobrecarga e atenção dos alunos. A pesquisa realiza uma revisão narrativa da literatura, enfocando oportunidades inovadoras para aprimorar o ensino e aprendizagem, com ênfase nas neurotecnologias como instrumentos promissores para compreender o desenvolvimento cognitivo dos estudantes.
Referências
Alivisatos, A. P., Chun, M., Church, G. M., Deisseroth, K., Donoghue, J. P., Greenspan, R. J., McEuen, P. L., Roukes, M. L., Sejnowski, T. J., Weiss, P. S., & Yuste, R. (2013). The Brain Activity Map. Science, 339(6125), 1284–1285. https://doi.org/10.1126/science.1236939
Al-Shargie, F., Tang, T. B., & Kiguchi, M. (2017). Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: An fNIRS-EEG study. Biomedical Optics Express, 8(5), 2583 – 2598. https://doi.org/10.1364/BOE.8.002583
Antonenko, P. D. (2018). Educational Neuroscience: Exploring Cognitive Processes that Underlie Learning. Em T. D., L. L., C. D. Parsons (Org.), Mind, Brain and Technology. Educational Communications and Technology: Issues and Innovations. (p. 27–46). Springer, Cham. https://doi.org/https://doi.org/10.1007/978-3-030-02631-8_3
Antonenko, P. D., & Niederhauser, D. S. (2010). The influence of leads on cognitive load and learning in a hypertext environment. Computers in Human Behavior, 26(2), 140–150. https://doi.org/10.1016/J.CHB.2009.10.014
Aricó, P., Sciaraffa, N., & Babiloni, F. (2020). Brain–Computer Interfaces: Toward a Daily Life Employment. Brain Sciences, 10. https://doi.org/https://doi.org/10.3390/brainsci10030157
Babiker, A., Faye, I., Mumtaz, W., Malik, A. S., & Sato, H. (2019). EEG in classroom: EMD features to detect situational interest of students during learning. Multimedia Tools and Applications, 78(12), 16261–16281. https://doi.org/10.1007/s11042-018-7016-z
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-Brain Synchrony and Learning Outcomes Vary by Student–Teacher Dynamics: Evidence from a Real-world Classroom Electroencephalography Study. Journal of Cognitive Neuroscience, 31(3), 401–411. https://doi.org/10.1162/jocn_a_01274
Bos, A. (2021). Universidade Federal do Rio Grande do Sul centro interdisciplinar de novas tecnologias na educação programa de pós-graduação em informática na educação andreia solange bos intensidade da atenção do estudante: registros de EEG no contexto de audiovisual e mídias interativas [Universidade Federal do Rio Grande do Sul]. http://hdl.handle.net/10183/235241
Bos, A. S., Herpich, F., Kuhn, I., Guarese, R. L. M., Tarouco, L. M. R., Zaro, M. A., Pizzato, M., & Wives, L. (2019). Educational Technology and Its Contributions in Students’ Focus and Attention Regarding Augmented Reality Environments and the Use of Sensors. Journal of Educational Computing Research, 57(7), 1832–1848. https://doi.org/10.1177/0735633119854033
Chen, C.-W., & Sun, C.-W. (2017). Combination of Electroencephalography and Near-Infrared Spectroscopy in Evaluation of Mental Concentration during the Mental Focus Task for Wisconsin Card Sorting Test. Scientific Reports, 7(1), 338. https://doi.org/10.1038/s41598-017-00448-6
Cicurel, R., & Nicolelis, A. L. M. (2015). O cérebro relativístico: Como ele funciona e por que ele não pode ser simulado por uma máquina de Turing. Kios Press.
Cui, X., Bray, S., Bryant, D. M., Glover, G. H., & Reiss, A. L. (2011). A quantitative comparison of NIRS and fMRI across multiple cognitive tasks. NeuroImage, 54(4), 2808–2821. https://doi.org/https://doi.org/10.1016/j.neuroimage.2010.10.069
Davidesco, I., Matuk, C., Bevilacqua, D., Poeppel, D., & Dikker, S. (2021). Neuroscience Research in the Classroom: Portable Brain Technologies in Education Research. Educational Researcher, 50(9), 649–656. https://doi.org/10.3102/0013189X211031563
Deutsch, D. (2000). A Essência da Realidade (1o ed, Vol. 1). Pearson Education.
Dikker, S., Michalareas, G., Oostrik, M., Serafimaki, A., Kahraman, H. M., Struiksma, M. E., & Poeppel, D. (2021). Crowdsourcing neuroscience: Inter-brain coupling during face-to-face interactions outside the laboratory. NeuroImage, 227, 117436. https://doi.org/10.1016/J.NEUROIMAGE.2020.117436
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J. J., Ding, M., & Poeppel, D. (2017). Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom. Current Biology, 27(9), 1375–1380. https://doi.org/10.1016/J.CUB.2017.04.002
Farahany, N. A. (2023). The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology (Vol. 1). St. Martin’s Publishing Group.
Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510–523. https://doi.org/10.1016/0013-4694(88)90149-6
Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., & Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Frontiers in Neuroscience, 8. https://doi.org/10.3389/fnins.2014.00385
Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of chiropractic medicine, 5(3), 101–117. https://doi.org/10.1016/S0899-3467(07)60142-6
Gupta, B., Sharma, R., Bansal, R., Soni, G. K., Negi, P., & Purdhani, P. (2023). An adaptive system for predicting student attentiveness in online classrooms. Indonesian Journal of Electrical Engineering and Computer Science, 31(2), 1136–1146. https://doi.org/10.11591/ijeecs.v31.i2.pp1136-1146
Gupta, C. N., & Palaniappan, R. (2013). Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study. https://doi.org/https://doi.org/10.1504/IJCPS.2013.053576
Gurnani, H., & Gajic, N. A. C. (2023). Signatures of task learning in neural representations. Current Opinion in Neurobiology, 83, 102759. https://doi.org/10.1016/J.CONB.2023.102759
Hazrati, M. Kh., Husin, H. M., & Hofmann, U. G. (2013). Wireless brain signal recordings based on capacitive electrodes. 2013 IEEE 8th International Symposium on Intelligent Signal Processing, 8–13. https://doi.org/https://doi.org/10.1109/WISP.2013.6657474
Hernández-Serrano, M. J. (2022). Neurotechnology in the classroom: Current research and future potential. https://www.revistacomunicar.com/pdf/76/presentacion-en.pdf
Hill, N. J., Gupta, D., Brunner, P., Gunduz, A., Adamo, M. A., Ritaccio, A., & Schalk, G. (2012). Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping. Journal of visualized experiments : JoVE, (64), 3993. https://doi.org/https://doi.org/10.3791/3993
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., Yao, Z., Eggermont, J., Höllt, T., Levi, B. P., Shehata, S. I., Aevermann, B., Beller, A., Bertagnolli, D., Brouner, K., … Lein, E. S. (2019). Conserved cell types with divergent features in human versus mouse cortex. Nature, 573(7772), 61–68. https://doi.org/10.1038/s41586-019-1506-7
Ienca, M., & Andorno, R. (2017). Towards new human rights in the age of neuroscience and neurotechnology. Life Sciences, Society and Policy, 13(1), 5. https://doi.org/10.1186/s40504-017-0050-1
Jessy, P. (2009). Analysis of EEG Signals for EEG-based Brain-Computer Interface. https://api.semanticscholar.org/CorpusID:60021022
Kandel, E. R., Markram, H., Matthews, P. M., Yuste, R., & Koch, C. (2013). Neuroscience thinks big (and collaboratively). Nature Reviews Neuroscience, 14(9), 659–664. https://doi.org/10.1038/nrn3578
Katona, J., & Kovari, A. (2015). EEG-based computer control interface for brain-machine interaction. International Journal of Online Engineering, 11(6), 43–48. https://doi.org/10.3991/ijoe.v11i6.5119
Kleih, S. C., Kaufmann, T., Zickler, C., Halder, S., Leotta, F., Cincotti, F., Aloise, F., Riccio, A., Herbert, C., Mattia, D., & Kübler, A. (2011). Out of the frying pan into the fire—the P300-based BCI faces real-world challenges. Progress in Brain Research, 194, 27–46. https://doi.org/10.1016/B978-0-444-53815-4.00019-4
Ko, L.-W., Komarov, O., Hairston, W. D., Jung, T.-P., & Lin, C.-T. (2017). Sustained Attention in Real Classroom Settings: An EEG Study. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00388
Lotte, F., Bougrain, L., Clerc, M., & Clerc Electroencephalography, M. (2015). Electroencephalography (EEG)-based Brain-Computer Interfaces. in Universal Access in Human- Computer Interaction. Applications and Services for Quality of Life, Introducing an information system for successful support of selective attention in online courses Springer, , 153–162. https://doi.org/10.1002/047134608X.W8278ï
Mak, J. N., Arbel, Y., Minett, J. W., McCane, L. M., Yuksel, B., Ryan, D., Thompson, D., Bianchi, L., & Erdogmus, D. (2011). Optimizing the P300-based brain–computer interface: current status, limitations and future directions. Journal of Neural Engineering, 8(2), 25003. https://doi.org/10.1088/1741-2560/8/2/025003
Nicolelis, M. (2017). Muito além do nosso eu: A nova Neurociência que une cérebro e máquinas e comno ela pode mudar nossas vidas. Editora Planeta.
Parpala, A., & Hailikari, T. (2021). How Can Student Experience Be Used to Raise the Academic Standards of Teaching? Em J. T. E. and P. A. and O. B. Shah Mahsood and Richardson (Org.), Assessing and Enhancing Student Experience in Higher Education (p. 191–209). Springer International Publishing. https://doi.org/10.1007/978-3-030-80889-1_8
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28(1), 377–401. https://doi.org/10.1146/annurev.neuro.27.070203.144216
Pilli, L., & Mazzon, J. A. (2016). Information overload, choice deferral, and moderating role of need for cognition: Empirical evidence. Revista de Administração, 51, 36–55. https://api.semanticscholar.org/CorpusID:73607689
Poulsen, A. T., Kamronn, S., Dmochowski, J., Parra, L. C., & Hansen, L. K. (2017). EEG in the classroom: Synchronised neural recordings during video presentation. Scientific Reports, 7(1), 43916. https://doi.org/10.1038/srep43916
Qian, T., Wu, W., Zhou, W., Gao, S., & Hong, B. (2011). ECoG based cortical function mapping using general linear model. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2347–2350. https://doi.org/10.1109/IEMBS.2011.6090656
Rezeika, A., Benda, M., Stawicki, P., Gembler, F., Saboor, A., & Volosyak, I. (2018). Brain-Computer Interface Spellers: A Review. Brain sciences, 8(4), 57. https://doi.org/https://doi.org/10.3390/brainsci8040057
Ribeiro, J. L P. (2014). Revisão de investigação e evidência cientifica. Psicologia, Saúde e Doenças, 15(3). https://www.redalyc.org/articulo.oa?id=36232744009
Rother, E. T. (2007). Revisão sistemática X revisão narrativa. Acta Paulista De Enfermagem, 20(2), v–vi. https://doi.org/10.1590/S0103-21002007000200001
Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1–13. https://doi.org/10.1080/00461520.2014.1002924
Stieglitz, T. (2021). Why Neurotechnologies? About the Purposes, Opportunities and Limitations of Neurotechnologies in Clinical Applications. Neuroethics, 14(1), 5–16. https://doi.org/10.1007/s12152-019-09406-7
Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251 – 296. https://doi.org/10.1023/A:1022193728205
Tang, Y.-W., & Lin, Y.-D. (2017). Brain Activity Monitoring System Based on EEG-NIRS Measurement System. Applied Mechanics and Materials, 870, 351–356. https://api.semanticscholar.org/CorpusID:116453419
Tokuhama-Espinosa, T. (2008). The Scientifically Substantiated Art of Teaching: A study in the development of standards in the new academic field of neuroeducation (mind, brain, and education science).
Tokuhama-Espinosa, T. (2015). The new science of teaching and learning: Using the best of mind, brain, and education science in the classroom. Columbia University’s Teachers College Press.
Tokuhama-Espinosa, T. (2017). Mind, Brain, and Education Science: An International Delph Survey 2016-2017. Quito, Ecuador: Author. https://doi.org/10.13140/RG.2.2.14259.22560
Trotter, E., & Roberts, C. A. (2006). Enhancing the early student experience. Higher Education Research & Development, 25(4), 371–386. https://doi.org/10.1080/07294360600947368
Wallois, F., Mahmoudzadeh, M., Patil, A., & Grebe, R. (2012). Usefulness of simultaneous EEG–NIRS recording in language studies. Brain and Language, 121(2), 110–123. https://doi.org/https://doi.org/10.1016/j.bandl.2011.03.010
Walter, C., Rosenstiel, W., Bogdan, M., Gerjets, P., & Spüler, M. (2017). Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment. Frontiers in Human Neuroscience, 11. https://www.frontiersin.org/articles/10.3389/fnhum.2017.00286
Wang, J., & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning. Computers in Human Behavior, 71, 79–89. https://doi.org/10.1016/J.CHB.2017.01.049
White, S. W., Richey, J. A., Gracanin, D., Bell, M. A., LaConte, S., Coffman, M., Trubanova, A., & Kim, I. (2015). The Promise of Neurotechnology in Clinical Translational Science. Clinical Psychological Science, 3(5), 797–815. https://doi.org/10.1177/2167702614549801
Williamson, B. (2019). Brain Data: Scanning, Scraping and Sculpting the Plastic Learning Brain Through Neurotechnology. Postdigital Science and Education, 1(1), 65–86. https://doi.org/10.1007/s42438-018-0008-5
Wolpaw, J. R., Millán, J. D. R., & Ramsey, N. F. (2020). Brain-computer interfaces: Definitions and principles. Handbook of clinical neurology, 168, 15–23. https://doi.org/https://doi.org/10.1016/B978-0-444-63934-9.00002-0
Yorke, M. (2000). The Quality of the Student Experience: What can institutions learn from data relating to non-completion? Quality in Higher Education, 6(1), 61–75. https://doi.org/10.1080/13538320050001072
Zaro, M. A., Rosat, R. M., Ribeiro, M. L. O., Spindola, M., Azevedo, A. M. P. de, Bonini-Rocha, A. C., & Timm, M. I. (2010). Emergência da Neuroeducação: a hora e a vez da neurociência para agregar valor à pesquisa educacional. Ciências & Cognição, 15, 199–210. http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S1806-58212010000100016&nrm=iso
Zhuang, J., & Ju, Y. S. (2013). Deployable MEMS Devices for Minimally Invasive Monitoring of Cortical Activities. https://api.semanticscholar.org/CorpusID:112020286
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