Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms

Misaki Kitahashi; Hisashi H; a

doi:10.20965/jaciii.2018.p0242

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JACIII Vol.22 No.2 pp. 242-248

doi: 10.20965/jaciii.2018.p0242

(2018)

Paper:

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Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms

Misaki Kitahashi and Hisashi Handa

Kindai Universsity
3-4-1 Kowakae, Higashi-Osaka 577-8502, Japan

Received:

August 27, 2017

Accepted:

January 11, 2018

Published:

March 20, 2018

Keywords:

classroom situations, learning analytics, convolutional neural networks

Abstract

The Learning Analytics is a research area that seeks to understand learning processes by using the various computer science techniques. In this paper, we focus on the analysis of certain classroom situations, such as lecturings, performing exercises, and testing. These analyses do not directory apply to students; however, they are very useful for analyzing and interpreting students’ behaviors in the classroom. This is significant as students’ behaviors can affect very real changes in classroom situations. This paper employs the Convolutional Neural Networks to identify various classroom situations from the spectrograms of environmental sounds in the classroom. Experimental results show the effectiveness of the proposed systems.

Cite this article as:

M. Kitahashi and H. Handa, “Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.2, pp. 242-248, 2018.

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References

[1] C. Lang, G. Siemens, A. Wise, and D. Gasevic, “Handbook of Learning Analytics,” Society for Learning Analytics Research, DOI: 10.18608/hla17, 2017.
[2] P. Bilkstein, “Multimodal learning analytics,” Proc. of the 3rd Int. Conf. on Learning Analytics and Knowledge (LAK’13), pp. 102-106, 2013.
[3] Z. Wang, X. Pan, K. F. Miller, and K. S. Cortina, “Automatic classification of activities in classroom discourse,” Computers and Education, Vol.78, pp. 115-123, 2014.
[4] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” MIT Press, 2016.
[5] M. Lin, Q. Chen, and S. Yan, “Network In Network,” https://arxiv.org/pdf/1312.4400v3.pdf, 2014.
[6] V. N. Vapnik, “Statistical Learning Theory,” John Wiley & Sons, 1998.
[7] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proc. of IEEE Computer Vision and Pattern Recognition, pp. 2169-2178, 2006.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] C. Lang, G. Siemens, A. Wise, and D. Gasevic, “Handbook of Learning Analytics,” Society for Learning Analytics Research, DOI: 10.18608/hla17, 2017.

[2] [2] P. Bilkstein, “Multimodal learning analytics,” Proc. of the 3rd Int. Conf. on Learning Analytics and Knowledge (LAK’13), pp. 102-106, 2013.

[3] [3] Z. Wang, X. Pan, K. F. Miller, and K. S. Cortina, “Automatic classification of activities in classroom discourse,” Computers and Education, Vol.78, pp. 115-123, 2014.

[4] [4] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” MIT Press, 2016.

[5] [5] M. Lin, Q. Chen, and S. Yan, “Network In Network,” https://arxiv.org/pdf/1312.4400v3.pdf, 2014.

[6] [6] V. N. Vapnik, “Statistical Learning Theory,” John Wiley & Sons, 1998.

[7] [7] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proc. of IEEE Computer Vision and Pattern Recognition, pp. 2169-2178, 2006.