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Classroom Teaching Behavior Analysis Based on Artificial Intelligence

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Artificial Intelligence in Education and Teaching Assessment
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Abstract

The integration of information technology and education has effectively promoted the improvement of the quality of education and teaching. However, traditional classroom teaching behavior analysis still uses manual observation methods, which is not only inefficient, but also subjectively affected by observers. To solve this problem, this paper uses the Mini-XCEPTION Networks model and the Long Short-Term Memory (LSTM) Networks model to put forward the classroom teaching behavior analysis based on artificial intelligence around two aspects of teacher expression and classroom atmosphere in the teaching process. This method greatly reduces the workload of statistical data and the impact of subjective judgment. In face expression recognition, we use the separable convolution depth and face recognition technology, real-time analysis of changes in the expression of teachers. For speech emotion recognition, we fused Mini-XCEPTION network model and the LSTM network model, by extracting features of speech mel-spectrogram, real-time analysis of changes in the classroom atmosphere. Through the final analysis results, teachers can fully grasp the classroom dynamics, create positive emotional communication in teacher-student interaction, and better optimize their classroom teaching behavior.

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References

  1. M. Zembylas, Emotional ecology: the intersection of emotional knowledge and pedagogical content knowledge in teaching. Teach. Teach. Educ. 23, 355–367 (2007)

    Article  Google Scholar 

  2. P. Ekman, W. Friesen, The Repertoire of Nonverbal Behavior: Categories, Origins, Usage, and Coding Semiotica, vol. 1, pp. 49–98 (1969)

    Google Scholar 

  3. R.E. Sutton, Teachers’ anger, frustration, and self-regulation, in Emotion in Education, pp. 259–274 (Academic Press, 2007)

    Google Scholar 

  4. E.S. Becker, T. Goetz, V. Morger et al., The importance of teachers’ emotions and instructional behavior for their students’ emotions–an experience sampling analysis. Teach. Teach. Educ. 43, 15–26 (2014)

    Article  Google Scholar 

  5. M. Arguedas, A. Daradoumis, F. Xhafa, Analyzing how emotion awareness influences students’ motivation, engagement, self-regulation and learning outcome. Educ. Technol. Soc. 19, 87–103 (2016)

    Google Scholar 

  6. R.E. Sutton, K.F. Wheatley, Teachers’ emotions and teaching: a review of the literature and directions for future research. Educ. Psychol. Rev. 15, 327–358 (2003)

    Article  Google Scholar 

  7. E. Spyrou, R. Nikopoulou, I. Vernikos et al., Emotion recognition from speech using the bag-of-visual words on audio segment spectrograms. Technologies 7, 20 (2019)

    Article  Google Scholar 

  8. L. Jie, Z. Xiaoyan, Z. Zhaohui, Speech emotion recognition of teachers in classroom teaching, in 2020 Chinese Control and Decision Conference (CCDC), pp. 5045–5050 (2020)

    Google Scholar 

  9. F. Chollet, Xception: deep learning with depthwise separable convolutions, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017)

    Google Scholar 

  10. S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, Aggregated residual transformations for deep neural networks, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995 (2017)

    Google Scholar 

  11. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Google Scholar 

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Acknowledgements

The work was supported by the Natural Science Foundation of China (61731006, 61971310).

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Correspondence to Wei Wang .

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Shi, S., Gao, J., Wang, W. (2021). Classroom Teaching Behavior Analysis Based on Artificial Intelligence. In: Wang, W., Wang, G., Ding, X., Zhang, B. (eds) Artificial Intelligence in Education and Teaching Assessment. Springer, Singapore. https://doi.org/10.1007/978-981-16-6502-8_3

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  • DOI: https://doi.org/10.1007/978-981-16-6502-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6501-1

  • Online ISBN: 978-981-16-6502-8

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