Electrophysiology and hyperscanning applied to e-learning for organizational training
Abstract
Purpose The purpose of this study is to investigate the effects of the remote training process on distance learning with the application of neurometrics and investigate the features of the training that promote better synchronization between trainers and trainees in terms of cognitive and emotional processes favorable to learning, during a condition of remote professional training. Design/methodology/approach The authors proposed a hyperscanning paradigm together with a conversational analysis to assess remote online training by collecting neurophysiological measures (frequency band analysis: delta, theta, alpha and beta) via multiple wearable electroencephalograms (EEGs) during a session of remote training. Findings Results showed increased delta activity in the trainer when he was leading the session and when the participants were exchanging feedback. The delivery of feedback was also linked to increased theta activity compared with the normal activity of the trainees. Finally, synchronization of EEG between trainer and trainee groups was found for the beta band. Research limitations/implications This study proposes to adopt a new multi-methodological approach that combines conversational analysis with the detection of remote neurometric parameters, in the field of educational neuroscience applied to organizational contexts. Practical implications Findings can help trainers in the development of their skills as trainers and in modeling remote training in organizations. Originality/value Findings highlight the crucial role of particular phases of the e-learning process, such as the feedback phase and the interaction trainer group, and they pointed out the relevance of neurophysiological measures to test the e-learning process.
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Electrophysiology and hyperscanning research provide insights into brain activity during learning, which AI systems can use to adapt learning environments based on real-time cognitive data.
“Electrophysiology and hyperscanning research provide insights into brain activity during learning, which AI systems can use to adapt learning environments based on real-time cognitive data [134].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [134]
Cites
Three Approaches to Qualitative Content Analysis
Hsieh, H.-F., & Shannon, S. E. (2005) · Qualitative Health Research · doi:10.1177/1049732305276687 · Cited by 44,601 · see citing works
The ten-twenty electrode system of the international federation
Jasper, H. H. (1958) · Electroencephalography and Clinical Neurophysiology · Cited by 6,567 · see citing works
Subjective wellbeing, health, and ageing
Steptoe, A., Deaton, A., & Stone, A. A. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61489-0 · Cited by 2,538 · see citing works
E-learning: Strategies for delivering knowledge in the digital age
Rosenberg, M. J., & Foshay, R. (2002) · Performance Improvement Journal · doi:10.1002/pfi.4140410512 · Cited by 2,079 · see citing works
Organizational Learning: From Experience to Knowledge
Argote, L., & Miron‐Spektor, E. (2011) · Organization Science · doi:10.1287/orsc.1100.0621 · Cited by 1,995 · see citing works
Brain Systems that Mediate both Emotion and Cognition
Gray, J. A. (1990) · Cognition & Emotion · doi:10.1080/02699939008410799 · Cited by 1,400 · see citing works
Gibson, C. B., & Gibbs, J. L. (2006) · Administrative Science Quarterly · doi:10.2189/asqu.51.3.451 · Cited by 1,152 · see citing works
Motivation, emotion, and their inhibitory control mirrored in brain oscillations
Knyazev, G. G. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.10.004 · Cited by 979 · see citing works
The Social Affordances of the Internet for Networked Individualism
Wellman, B., Quan‐Haase, A., Boase, J., Chen, W., Hampton, K. N., Díaz, I., & Miyata, K. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2003.tb00216.x · Cited by 896 · see citing works
Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J. J., Ding, M., et al. (2017) · Current Biology · doi:10.1016/j.cub.2017.04.002 · Cited by 871 · see citing works
Frontal midline EEG dynamics during working memory
Onton, J., Arnaud Delorme, A., & Makeig, S. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.04.014 · Cited by 846 · see citing works
EEG delta oscillations as a correlate of basic homeostatic and motivational processes
Knyazev, G. G. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.10.002 · Cited by 776 · see citing works
Ardichvili, A. A. (2008) · Advances in Developing Human Resources · doi:10.1177/1523422308319536 · Cited by 473 · see citing works
Frontal theta EEG activity correlates negatively with the default mode network in resting state
Scheeringa, R., Bastiaansen, M. C. M., Petersson, K. M., Oostenveld, R., Norris, D. G., & Hagoort, P. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2007.05.017 · Cited by 400 · see citing works
Gola, M. K., Magnuski, M., Szumska, I., & Wróbel, A. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.05.007 · Cited by 368 · see citing works
Beta activity: a carrier for visual attention
Wróbel, A. (2000) · Acta Neurobiologiae Experimentalis · doi:10.55782/ane-2000-1344 · Cited by 324 · see citing works
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01274 · Cited by 312 · see citing works
Students’ perceptions toward online learning and face-to-face learning courses
Balı, S. Z., & Liu, M. C. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1108/1/012094 · Cited by 306 · see citing works
Investigating EEG-based functional connectivity patterns for multimodal emotion recognition
Wu, X., Zheng, W., Li, Z., & Lu, B. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac49a7 · Cited by 244 · see citing works
EEG theta and gamma responses to semantic violations in online sentence processing
Hald, L. A., BASTIAANSEN, M. C. M., & Peter Hagoort, A. (2005) · Brain and Language · doi:10.1016/j.bandl.2005.06.007 · Cited by 242 · see citing works
Event-related delta and theta synchronization during explicit and implicit emotion processing
Knyazev, G. G., Slobodskoj‐Plusnin, J. Y., & Bocharov, A. V. (2009) · Neuroscience · doi:10.1016/j.neuroscience.2009.09.057 · Cited by 241 · see citing works
Balconi, M., Grippa, E., & Vanutelli, M. E. (2015) · Brain and Cognition · doi:10.1016/j.bandc.2015.02.001 · Cited by 227 · see citing works
Self-citationFell, J., Ludowig, E., Staresina, B. P., Wagner, T., Kranz, T., Elger, C. E., & Axmacher, N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3668-10.2011 · Cited by 203 · see citing works
The role of IT-based technologies on the management of human resources in the COVID-19 era
Vahdat, S. (2021) · Kybernetes · doi:10.1108/k-04-2021-0333 · Cited by 196 · see citing works
Heidari, A., Jafari Navimipour, N., Unal, M., & Zhang, G. (2023) · ACM Computing Surveys · doi:10.1145/3571728 · Cited by 195 · see citing works
Khader, P. H., Jost, K., Ranganath, C., & Rösler, F. (2009) · Neuroscience Letters · doi:10.1016/j.neulet.2009.11.028 · Cited by 186 · see citing works
Inter-brain synchrony in teams predicts collective performance
Reinero, D. A., Dikker, S., & Bavel, J. J. V. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa135 · Cited by 174 · see citing works
Does online technology make us more or less sociable? A preliminary review and call for research
Waytz, A., & Gray, K. (2018) · Perspectives on Psychological Science · doi:10.1177/1745691617746509 · Cited by 157 · see citing works
The Social Complexity of Organizational Learning: The Dynamics of Learning and Organizing
Antonacopoulou, E. P., & Chiva, R. (2007) · Management Learning · doi:10.1177/1350507607079029 · Cited by 153 · see citing works
E-learning in Saudi Arabia: Past, present and future
Al-Asmari, A. M., & Khan, M. S. R. (2014) · Near and Middle Eastern Journal of Research in Education · doi:10.5339/nmejre.2014.2 · Cited by 118 · see citing works
Yang, J., Peng, M. Y., Wong, S.-H., & Chong, W.-L. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.584976 · Cited by 118 · see citing works
The Meaning of Organizational Learning
Popova-Nowak, I. V., & Cseh, M. (2015) · Human Resource Development Review · doi:10.1177/1534484315596856 · Cited by 110 · see citing works
Surviving COVID-19: The Neuroscience of Smart Working and Distance Learning
Riva, G., Wiederhold, B. K., & Mantovani, F. (2021) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2021.0009 · Cited by 109 · see citing works
Resting EEG in alpha and beta bands predicts individual differences in attentional blink magnitude
MacLean, M. H., Arnell, K. M., & Côté, K. A. (2012) · Brain and Cognition · doi:10.1016/j.bandc.2011.12.010 · Cited by 106 · see citing works
Delta responses and cognitive processing: single-trial evaluations of human visual P300
Schürmann, M., Başar‐Eroğlu, C., Kolev, V., & Başar, E. (2001) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(00)00144-6 · Cited by 105 · see citing works
Balconi, M., Fronda, G., & Crivelli, D. (2018) · Stress · doi:10.1080/10253890.2018.1531845 · Cited by 89 · see citing works
Self-citationSocial anxiety and physiological arousal during computer mediated vs. face to face communication
Shalom, J. G., Israeli, H., Markovitzky, O., & Lipsitz, J. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.11.056 · Cited by 87 · see citing works
Using distraction to regulate emotion: Insights from EEG theta dynamics
Uusberg, A., Thiruchselvam, R., & Gross, J. J. (2014) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2014.01.006 · Cited by 80 · see citing works
Neuroscience: Viable Applications in Education?
Devonshire, I. M., & Dommett, E. J. (2010) · The Neuroscientist · doi:10.1177/1073858410370900 · Cited by 76 · see citing works
Kulikowski, K., Przytuła, S., & Sułkowski, Ł. (2021) · Sustainability · doi:10.3390/su13052752 · Cited by 67 · see citing works
Balconi, M., Pézard, L., Nandrino, J., & Vanutelli, M. E. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187652 · Cited by 66 · see citing works
Self-citationA green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios
Heidari, A., Navimipour, N. J., Jamali, M. A. J., & Akbarpour, S. (2023) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2023.100859 · Cited by 66 · see citing works
Tortella, G., Seabra, A. B., Padrão, J., & Juan, R. D.-S. (2021) · Brain Sciences · doi:10.3390/brainsci11050552 · Cited by 58 · see citing works
Balconi, M., & Lucchiari, C. (2005) · Genetic Social and General Psychology Monographs · doi:10.3200/mono.131.1.41-69 · Cited by 48 · see citing works
Self-citationKaplan‐Rakowski, R. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09897-9 · Cited by 46 · see citing works
Balconi, M., & Fronda, G. (2020) · Brain Sciences · doi:10.3390/brainsci10010029 · Cited by 38 · see citing works
Self-citationPanjaburee, P., Komalawardhana, N., & Ingkavara, T. (2022) · Journal of Computers in Education · doi:10.1007/s40692-021-00216-6 · Cited by 37 · see citing works
Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes
Bröhl, F., & Kayser, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117958 · Cited by 36 · see citing works
Venturella, I., Gatti, L., Vanutelli, M. E., & Balconi, M. (2017) · Neuropsychological Trends · doi:10.7358/neur-2017-021-vent · Cited by 32 · see citing works
Self-citationYanson, R., & Johnson, R. D. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.05.010 · Cited by 30 · see citing works
Attention Protects the Fidelity of Visual Memory: Behavioral and Electrophysiological Evidence
Jie, H. J., & Sekuler, R. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2560-10.2010 · Cited by 25 · see citing works
Functional brain connectivity when cooperation fails
Balconi, M., Vanutelli, M. E., & Gatti, L. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.02.009 · Cited by 25 · see citing works
Self-citationBalconi, M., Fronda, G., Cassioli, F., & Crivelli, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263668 · Cited by 20 · see citing works
Self-citationChanges in EEG Alpha Activity during Attention Control in Patients: Association with Sleep Disorders
Евгеньевна, Р. А., Selskii, A. O., Kiselev, A. R., Shamionov, R. М., Parsamyan, R. R., & Zhuravlev, M. О. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11070601 · Cited by 18 · see citing works
Immersive Virtual Environments in Corporate Education and Training.
Queiroz, A. C. M., Nascimento, A., Tori, R., Brashear, T. G., de Melo, V. V., Meirelles, F. D. S., & Leme, M. I. D. S. (2018) · Americas Conference on Information Systems · Cited by 16 · see citing works
Distance Learning System Design in Edge Network
Pei, W., Li, J., Li, B., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-8462-6_148 · Cited by 2 · see citing works
Duggan, M. M. (2018) · School for International Training Digital Collections (School for International Training) · Cited by 1 · see citing works
Big Data: neuroscience at the service of management
Cassioli, F., & Balconi, M. (2020) · IRCCAN · doi:10.7359/945-2020-cass · Cited by 1 · see citing works
Self-citation
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Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“Electrophysiology and hyperscanning research provide insights into brain activity during learning, which AI systems can use to adapt learning environments based on real-time cognitive data [134].”
- Authors
- Balconi, M., Angioletti, L., & Cassioli, F.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- Venue
- The Learning Organization
- Publisher
- Emerald
- Published
- 2023-07-19
- Volume
- 30
- Issue
- 6
- Pages
- 857-876
- ISSN
- 0969-6474 (ISSN-L)
- OpenAlex
- W4384575653
- Cited by
- 12
- References
- 58
- Citations by year
- 2026: 3 · 2024: 8 · 2023: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Learning Organization (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Education › Online and Blended Learning
- Keywords
- remote training, online learning, employee training, hyperscanning, EEG synchronization, feedback, delta oscillations, theta activity, beta band, neuroeducation, wearable EEG, conversation analysis
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