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.

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

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)