TY - JOUR
T1 - A Novel Intelligence Evaluation Framework
T2 - Exploring the Psychophysiological Patterns of Gifted Students
AU - Shen, Jian
AU - Zhu, Kexin
AU - Zhao, Zeguang
AU - Liang, Huajian
AU - Ma, Yu
AU - Qian, Kun
AU - Zhang, Yanan
AU - Dong, Qunxi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024/4/1
Y1 - 2024/4/1
N2 - Intelligence evaluation is a desirable intelligent application for sensing and interaction in various scenarios, e.g., education, office, and the aviation industry. For example, identifying gifted students, who learn faster and more efficiently than general students due to their neurophysiological advantages, and teaching different students according to their intelligence are urgent requirements in school education. However, current intelligence evaluation mainly relies on intelligence quotient (IQ) tests, which have a problem of decreasing reliability in repeated tests. In addition, no objective assessment criteria are available in the present intelligence evaluation process. Electroencephalogram (EEG) signals, which reflect the neuroelectrical activities of the brain, can be utilized to develop an objective and promising tool for investigating the neurophysiological advantages of gifted groups and augmenting the effects of intelligence evaluation. Consequently, we proposed a novel real-time intelligence evaluation framework based on users' psychophysiological data. Then, we leveraged the framework to investigate a case study to asses which EEG patterns could be used to effectively characterize gifted students and distinguish them from average students. Experimental results reveal the great differences in the chaos degree of the brain (CDB) between different groups of subjects and the effectiveness of the model in identifying gifted students, thus verifying the practicability and validity of the proposed framework.
AB - Intelligence evaluation is a desirable intelligent application for sensing and interaction in various scenarios, e.g., education, office, and the aviation industry. For example, identifying gifted students, who learn faster and more efficiently than general students due to their neurophysiological advantages, and teaching different students according to their intelligence are urgent requirements in school education. However, current intelligence evaluation mainly relies on intelligence quotient (IQ) tests, which have a problem of decreasing reliability in repeated tests. In addition, no objective assessment criteria are available in the present intelligence evaluation process. Electroencephalogram (EEG) signals, which reflect the neuroelectrical activities of the brain, can be utilized to develop an objective and promising tool for investigating the neurophysiological advantages of gifted groups and augmenting the effects of intelligence evaluation. Consequently, we proposed a novel real-time intelligence evaluation framework based on users' psychophysiological data. Then, we leveraged the framework to investigate a case study to asses which EEG patterns could be used to effectively characterize gifted students and distinguish them from average students. Experimental results reveal the great differences in the chaos degree of the brain (CDB) between different groups of subjects and the effectiveness of the model in identifying gifted students, thus verifying the practicability and validity of the proposed framework.
KW - Chaos degree of the brain (CDB)
KW - electroencephalogram (EEG) signals
KW - gifted students
KW - intelligence evaluation
KW - psychophysiological data
UR - http://www.scopus.com/inward/record.url?scp=85168723933&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2023.3303331
DO - 10.1109/TCSS.2023.3303331
M3 - Article
AN - SCOPUS:85168723933
SN - 2329-924X
VL - 11
SP - 2036
EP - 2045
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 2
ER -