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Estimation of objective understanding measure based on student’s nonverbal behavior recognition in a person-to-person teaching situation
Author(s): Ryo Miyoshi; Koichi Taguchi; Manabu Hashimoto
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Paper Abstract

In this paper, we propose using image recognition techniques to estimate the “understanding measure” in person-toperson teaching situations. The phrase “understanding measure” refers to how strongly a teacher feels a student understands a topic. First, we extract a student’s nonverbal behavior (head movement, gazes, and blinking) as the features for the estimation process. Next, we calculate the subspace from the aforementioned feature by using principal component analysis (PCA) and linear discriminant analysis (LDA). Finally, we classify unknown data as either “understood” or “did not understand” by using a kNN classifier in subspace. Our experiments confirmed that the Fmeasure of the classification “understood” by our method was 0.75 and “did not understand” was 0.60, indicating that our method improved F-measures 0.38 and 0.11, respectively, compared with previous methods.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493A (22 March 2019); doi: 10.1117/12.2521616
Show Author Affiliations
Ryo Miyoshi, Chukyo Univ. (Japan)
Koichi Taguchi, Chukyo Univ. (Japan)
Manabu Hashimoto, Chukyo Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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