Abstract
It is generally known that learning interest plays the key role on affecting learners’ performance. Previous research methods of learning interest are mostly based on questionnaire survey and the analysis of learners’ behavioral data. Considering the defects such as inaccuracy of subjective statements and limitations of samples, this paper proposed a new method for the learner’s interest mining from their EEG signals through neural activity observation. It analyzed the neural activities in learning process, and designed the situational interest experiment according to learners’ brain neural mechanism. Firstly, the experimental results of EEG test of thirty-six participants verify that animation, image and speech are superior to texts in terms of cognitive attention time, and provide the cognitive neural basis for the design and display of learning resources. In addition, the experimental results show that the eigenvalues of wavelet entropy and standard deviation obtained from EEG signals appear significant difference under the state of interest. When the cognitive load is higher, the relative energy of the frontal and temporal regions will be enhanced. Research findings of this paper provide the neural observation method and characteristic parameters to find learning interest more accurately and effectively than traditional methods.
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Acknowledgements
This work is supported by Humanities and Social Sciences Project of Ministry of Education of China (No. 18YJA630019), International Cooperative Program of Shanghai Municipal Science and Technology Commission of China (No. 16550720500), Shanghai Philosophy and Social Sciences Plan (No. 2018BGL023), and Association of Fundamental Computing Education in Chinese Universities (2018-AFCEC-119). Special thanks to Dr. Hongzhi Hu for her assistance to Prof. Weihui Dai who is the corresponding author of this paper.
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Dai, Y., Chen, J., Chen, H., Lu, S., Fu, Y., Dai, W. (2019). Study on Learner’s Interest Mining Based on EEG Signal Analysis. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_29
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DOI: https://doi.org/10.1007/978-981-13-3044-5_29
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