| Autor: |
Nuraini Jamil, Abdelkader Nasreddine Belkacem |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 80086-80098 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3409771 |
| Popis: |
Remote phenomenon is of expanding significance due to the increasing popularity of distance education. In this preliminary study, we examines the occurrence of brain-to-brain synchronization during remote learning using electroencephalography (EEG). Participants actively participated in a remote learning challenge by attending synchronous online classes, while their EEG data was captured. The work seeks to comprehend synchronization and intermittent desynchronization through state-of-the-art neuroimaging and K-nearest Neighbors (KNN) algorithm. By analyzing data obtained from EEG, it is feasible to evaluate the level of brainwave synchronization between teachers and students using correlation coefficients. For educators, the study’s findings provide valuable insights that can be used to improve instructional techniques and guide them through the ever-changing digital ecosystem. Understanding ideal synchronization times and the factors contributing to desynchronization makes it possible to design therapies that enhance cognitive resonance. This multidisciplinary work crosses the gap between education and neuroscience, and contributes to clarifying the significance of brain-to-brain synchronization in remote learning and highlighting the role played by ML in resolving cognitive alignment issues. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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