A Novel Intelligence Evaluation Framework: Exploring the Psychophysiological Patterns of Gifted Students

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 a...

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Bibliographic Details
Published in:IEEE transactions on computational social systems 2024-04, Vol.11 (2), p.2036-2045
Main Authors: Shen, Jian, Zhu, Kexin, Zhao, Zeguang, Liang, Huajian, Ma, Yu, Qian, Kun, Zhang, Yanan, Dong, Qunxi
Format: Article
Language:English
Subjects:
Behavioral sciences
Brain
Brain modeling
Chaos degree of the brain (CDB)
Cloud computing
Education
electroencephalogram (EEG) signals
Electroencephalography
gifted students
Intelligence
intelligence evaluation
Physiology
psychophysiological data
Real-time systems
Students
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Summary: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.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2023.3303331