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Inter-brain coupling reflects disciplinary differences in real-world classroom learning
Educationnpj Science of Learning

Inter-brain coupling reflects disciplinary differences in real-world classroom learning

J. Chen, P. Qian, et al.

Discover groundbreaking insights into how brain activity influences learning success in Chinese and Math classes, revealing that inter-brain coupling may serve as key neural correlates for success in different disciplines. This intriguing research was conducted by Jingjing Chen, Penghao Qian, Xinqiao Gao, Baosong Li, Yu Zhang, and Dan Zhang.... show more
Abstract
The classroom is the primary site for learning. A vital feature of classroom learning is the division of educational content into various disciplines. While disciplinary differences could substantially influence the learning process toward success, little is known about the neural mechanism underlying successful disciplinary learning. In the present study, wearable EEG devices were used to record a group of high school students during their classes of a soft (Chinese) and a hard (Math) discipline throughout one semester. Inter-brain coupling analysis was conducted to characterize students' classroom learning process. The students with higher scores in the Math final exam were found to have stronger inter-brain couplings to the class (i.e., all the other classmates), whereas the students with higher scores in Chinese were found to have stronger inter-brain couplings to the top students in the class. These differences in inter-brain couplings were also reflected in distinct dominant frequencies for the two disciplines. Our results illustrate disciplinary differences in the classroom learning from an inter-brain perspective, suggesting that an individual's inter-brain coupling to the class and to the top students could serve as potential neural correlates for successful learning in hard and soft disciplines correspondingly.
Publisher
npj Science of Learning
Published On
Jan 01, 2023
Authors
Jingjing Chen, Penghao Qian, Xinqiao Gao, Baosong Li, Yu Zhang, Dan Zhang
Tags
neural mechanismslearninginter-brain couplingEEGChineseMathbrainwave frequencies
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