Keywords
Neuroeducation, electroencephalography, neurophysiological measurements in education, primary education, elementary school, educational contex, case study
Abstract
Wireless electroencephalography (EEG) devices allow for recordings in contexts outside the laboratory. However, many details must be considered for their use. In this research, using a case study with a group of third-grade primary school students, we aim to show some of the potentialities and limitations of research with these devices in educational settings. Several balances are apparent in the development of these experiences: between the interests and possibilities of the research teams and the educational communities; between the distortion of life in the classrooms and the opportunities for collaboration between academia and practice; and between the budget and the ease of preparing the equipment and the usefulness of the collected data. Among their potentialities is the knowledge that they allow access to different cognitive and emotional processes, and the learning opportunity represented by the links between researchers and educational communities. Life in the classrooms is interrupted by these types of experiences, but this can be a cost that facilitates more integrated future developments that benefit teaching and learning processes.
References
Akalin-Acar, Z., & Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain topography, 26(3), 378-396. https://doi.org/10.1007/s10548-012-0274-6
Link DOI | Link Google Scholar
Antonenko, P., Paas, F., Grabner, R., & Van-Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425-438. https://doi.org/10.1007/s10648-010-9130-y
Link DOI | Link Google Scholar
Basar, E., Basar-Eroglu, C., Karakas, S., & Schürmann, M. (1999). Oscillatory brain theory: A new trend in neuroscience. IEEE engineering in medicine and biology magazine: the quarterly magazine of the Engineering in Medicine & Biology Society, 18(3), 56-66. https://doi.org/10.1109/51.765190
Link DOI | Link Google Scholar
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), 401-11. https://doi.org/10.1162/jocn_a_01274
Link DOI | Link Google Scholar
Browarska, N., Kawala-Sterniuk, A., Zygarlicki, J., Podpora, M., Pelc, M., Martinek, R., & Gorzelanczyk, E.J. (2021). Comparison of smoothing filters' influence on quality of data recorded with the emotiv EPOC Flex brain-computer interface headset during audio stimulation. Brain sciences, 11(1), 98. https://doi.org/10.3390/brainsci11010098
Link DOI | Link Google Scholar
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189X018001032
Link DOI | Link Google Scholar
Coan, J.A., & Allen, J.J. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology, 67(1-2), 7-50. https://doi.org/10.1016/j.biopsycho.2004.03.002
Link DOI | Link Google Scholar
Craik, A., He, Y., & Contreras-Vidal, J.J. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(3), 031001. https://doi.org/10.1088/1741-2552/ab0ab5
Link DOI | Link Google Scholar
Dikker, S., Haegens, S., Bevilacqua, D., Davidesco, I., Wan, L., Kaggen, L., McClintock, J., Chaloner, K., Ding, M., West, T., & Poeppel, D. (2020). Morning brain: Real-world neural evidence that high school class times matter. Social Cognitive and Affective Neuroscience, 15(11), 1193-1202. https://doi.org/10.1093/scan/nsaa142
Link DOI | Link Google Scholar
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), 1375-80. https://doi.org/10.1016/j.cub.2017.04.002
Link DOI | Link Google Scholar
Glaser, B., & Strauss, A. (2006). The discovery of grounded theory. Aldine Transaction.
Link Google Scholar
Grammer, J.K., Xu, K., & Lenartowicz, A. (2021). Effects of context on the neural correlates of attention in a college classroom. NPJ science of learning, 6(1), 15. https://doi.org/10.1038/s41539-021-00094-8
Link DOI | Link Google Scholar
Hajare, R., & Kadam, S. (2021). Comparative study analysis of practical EEG sensors in medical diagnoses. Global Transitions Proceedings, 2(2), 467-475. https://doi.org/10.1016/j.gltp.2021.08.009
Link DOI | Link Google Scholar
Howard-Jones, P.A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., Laurillard, D., & Thomas, M.S.C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 620-627. https://doi.org/10.1037/rev0000036
Link DOI | Link Google Scholar
Janssen, T.W.P., Grammer, J.K., Bleichner, M.G., Bulgarelli, C., Davidesco, I., Dikker, S., Jasi?ska, K.K., Siugzdaite, R., Vassena, E., Vatakis, A., Zion-Golumbic, E., & van Atteveldt, N. (2021). Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience. Mind, Brain and Education, 15(4), 354-370. https://doi.org/10.1111/mbe.12302
Link DOI | Link Google Scholar
Katzir, T., & Paré-Blagoev, J. (2006). Applying cognitive neuroscience research to education: The case of literacy. Educational Psychologist, 41(1), 53-74. https://doi.org/10.1207/s15326985ep4101_6
Link DOI | Link Google Scholar
Khedher, A.B., Jraidi, I., & Frasson, C. (2019). Tracking students’ mental engagement using EEG signals during an interaction with a virtual learning environment. Journal of Intelligent Learning Systems and Applications, 11(1), 1-14. https://doi.org/10.4236/jilsa.2019.111001
Link DOI | Link Google Scholar
Krigolson, O.E., Williams, C.C., Norton, A., Hassall, C.D., & Colino, F.L. (2017). Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research. Frontiers in Neuroscience, 11, 109. https://doi.org/10.3389/fnins.2017.00109
Link DOI | Link Google Scholar
Lau-Zhu, A., Lau, M.P.H., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36, 100635. https://doi.org/10.1016/j.dcn.2019.100635
Link DOI | Link Google Scholar
Liu, Y., & Zhang, Y. (2021). Developing sustaining authentic partnership between MBE researchers and local schools. Mind, Brain, and Education, 15(2), 153-162. https://doi.org/10.1111/mbe.12280
Link DOI | Link Google Scholar
Mason L. (2009). Bridging neuroscience and education: A two-way path is possible. Cortex, 45(4), 548-549. https://doi.org/10.1016/j.cortex.2008.06.003
Link DOI | Link Google Scholar
Matusz, P.J., Dikker, S., Huth, A.G., & Perrodin, C. (2019). Are we ready for real-world neuroscience? Journal of Cognitive Neuroscience, 31(3), 327-338. https://doi.org/10.1162/jocn_e_01276
Link DOI | Link Google Scholar
McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating player task engagement and arousal using electroencephalography. Procedia Manufacturing, 3, 2303-2310. https://doi.org/10.1016/j.promfg.2015.07.376
Link DOI | Link Google Scholar
Pope, A.T., Bogart, E.H., & Bartolome, D.S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1-2), 187-195. https://doi.org/10.1016/0301-0511(95)05116-3
Link DOI | Link Google Scholar
Rose, N., & Abi-Rached, J. (2014). Governing through the brain: Neuropolitics, neuroscience and subjectivity. The Cambridge Journal of Anthropology, 32(1), 3-23. https://doi.org/10.3167/ca.2014.320102
Link DOI | Link Google Scholar
Shad, E.H.T., Molinas, M., & Ytterdal, T. (2020). Impedance and noise of passive and active dry EEG electrodes: a review. IEEE Sensors Journal, 20(24), 14565-14577. https://doi.org/10.1109/JSEN.2020.3012394
Link DOI | Link Google Scholar
Shamay-Tsoory, S.G., & Mendelsohn, A. (2019). Real-life neuroscience: An ecological approach to brain and behavior research. Perspectives on Psychological Science, 14(5), 841-859. https://doi.org/10.1177/1745691619856350
Link DOI | Link Google Scholar
Shkedi, A. (2004). Second?order theoretical analysis: A method for constructing theoretical explanation. International Journal of Qualitative Studies in Education, 17(5), 627-646. https://doi.org/10.1080/0951839042000253630
Link DOI | Link Google Scholar
Stake, R.E. (2010). Qualitative research: Studying how things work. Guilford Publications. https://bit.ly/3J0mmNf
Link Google Scholar
Vekety, B., Logemann, A., & Takacs, Z.K. (2022). Mindfulness practice with a brain-sensing device improved cognitive functioning of elementary school children: An exploratory pilot study. Brain Sciences, 12(1), 103. https://doi.org/10.3390/brainsci12010103
Link DOI | Link Google Scholar
Williams, N.S., McArthur, G.M., & Badcock, N.A. (2020a). 10 years of EPOC: A scoping review of Emotiv’s portable EEG device. BioRxiv. https://doi.org/10.1101/2020.07.14.202085
Link DOI | Link Google Scholar
Williams, N.S., McArthur, G.M., de-Wit, B., Ibrahim, G., & Badcock, N.A. (2020b). A validation of Emotiv EPOC Flex saline for EEG and ERP research. PeerJ, 8, e9713. https://doi.org/10.7717/peerj.9713
Link DOI | Link Google Scholar
Williamson, B. (2018). Brain data: Scanning, scraping and sculpting the plastic learning brain through neurotechnology. Postdigital Science and Education, 1, 65-86. https://doi.org/10.1007/s42438-018-0008-5
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/10.1111/mbe.12314
Link DOI | Link Google Scholar
Xu, K., Torgrimson, S.J., Torres, R., Lenartowicz, A., & Grammer, J.K. (2022). EEG data quality in real?world settings: Examining neural correlates of attention in school?aged children. Mind, Brain, and Education, 16(3), 221-227. https://doi.org.ponton.uva.es/10.1111/mbe.12314
Link DOI | Link Google Scholar
Zerafa, R., Camilleri, T., Falzon, O., & Camilleri, K.P. (2018). A comparison of a broad range of EEG acquisition devices– is there any difference for SSVEP BCIs? Brain-Computer Interfaces, 5(4), 121-131 https://doi.org/10.1080/2326263X.2018.1550710
Link DOI | Link Google Scholar
Technical information
Received: 28-12-2022
Revised: 18-01-2023
Accepted: 23-02-2023
OnlineFirst: 30-05-2023
Publication date: 01-07-2023
Article revision time: 21 days | Average time revision issue 76: -6 days
Article acceptance time: 57 days | Average time of acceptance issue 76: 72 days
Preprint editing time: 140 days | Average editing time preprint issue 76: 155 days
Article editing time: 185 days | Average editing time issue 76: 200 days
Metrics
Metrics of this article
Views: 56170
Abstract readings: 55169
PDF downloads: 1001
Full metrics of Comunicar 76
Views: 485032
Abstract readings: 473418
PDF downloads: 11614
Cited by
Cites in Web of Science
Currently there are no citations to this document
Cites in Scopus
Currently there are no citations to this document
Cites in Google Scholar
Currently there are no citations to this document