RESEARCH ARTICLE
Brain Imaging in Mathematics Education: An Analysis of KCI Studies
Doyeon Ahn1, Sunhong Yang2, Kwang-Ho Lee3*
1Cheonan-cheongdang Elementary School
2DaeJoen-daehung Elementary School
3Korea National University of Education
Correspondence to paransol@knue.ac.kr
Brain, Digital, & Learning. Volume 13, Number 4, 503–518, December 2023. https://doi.org/10.31216/BDL.20230029
Received on December 4, 2023, Revised on December 12, 2023, Accepted on December 14, 2023, Published on December 31, 2023.
Copyright © 2023. Institute of Brain based Education, Korea National University of Education This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study reviews the application of EEG, fMRI, and fNIRS in mathematics education research in South Korea, analyzing 16 significant papers from the last 25 years. The results demonstrate a significant relationship between brainwave patterns and various aspects of mathematical learning. EEG studies highlighted the role of different brainwaves in math anxiety, computation, and problem-solving. fMRI research revealed the importance of whole brain activation in spatial and numerical processing. Studies using fNIRS emphasized the prefrontal cortex’s involvement in mathematical tasks, particularly in spatial and computational tasks. Conclusions highlight the need for math learning programs that enhance brain connectivity and consider problem difficulty and learner preferences. The study advocates for more research on the prefrontal cortex, particularly in relation to math anxiety, and emphasizes the importance of brain imaging techniques in understanding cognitive processes in math education, suggesting an integrated approach in future research.
Keywords
Mathematical education, brain imaging techniques, EEG, fMIR, fNIRS