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Estimating Children’s Intrinsic Motivation During a Class Based on Face Orientation and Gaze Information

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Advances in Artificial Intelligence (JSAI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1423))

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Abstract

Considering that non-cognitive abilities develop significantly during childhood, the Japanese early childhood education community has recently begun to focus on the study of non-cognitive abilities. However, measurement and estimation methods for non-cognitive abilities face quantitative and objective challenges. In addition, there is little research on the active use of artificial intelligence technology in education. Thus, this study proposed a method for quantitatively estimating concentration in the classroom, one of the children’s non-cognitive abilities. Specifically, it examined the number of intersections between children’s gazes using information on the direction of their faces and their gazes during class and determined whether all children should focus their gaze on a small area. Moreover, it quantitatively examined whether each child behaved appropriately in different situations by measuring the angle between each child’s gaze and the center of gravity of the intersections of all the children’s gazes. As a result, we found that we could capture the characteristics of each child’s behavior.

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References

  1. Gutman, L., Schoon, I.: The impact of non-cognitive skills on outcomes for young people: literature review. Education Endowment Foundation, London (2013)

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  3. Miyata, M., Yamada, T., Takamoto, M., Morishita, Y., Omori, T.: Estimation of student’s characteristics during classroom activities using face information. In: The 38th JSET Spring Conference (2021). (in Japanese)

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP19H01718 and by the Canon Foundation Research Grant Program “Science and Technology that Achieve a Good Future”.

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Authors and Affiliations

  1. Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto, Japan

    Keisuke Morita, Natsuki Oka & Kazuaki Tanaka

  2. Tamagawa University, Tamagawagakuen, Machida, Tokyo, Japan

    Masahiro Miyata & Takashi Omori

Authors
  1. Keisuke Morita
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  2. Natsuki Oka
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  3. Kazuaki Tanaka
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  4. Masahiro Miyata
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  5. Takashi Omori
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Corresponding author

Correspondence to Natsuki Oka .

Editor information

Editors and Affiliations

  1. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  2. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  3. Faculty of Business and Commerce, Kansai University, Osaka, Japan

    Katsutoshi Yada

  4. Faculty of Informatics, Kansai University, Takatsuki, Osaka, Japan

    Mitsunori Matsushita

  5. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  6. Division of Behavioral Science, Chiba University, Chiba, Japan

    Akinori Abe

  7. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

  8. Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan

    Toshihiro Hiraoka

  9. Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  10. Graduate School of Information Science and Technology Sapporo Hokkaido Japan, Hokkaido University, Sapporo, Hokkaido, Japan

    Rafal Rzepka

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Morita, K., Oka, N., Tanaka, K., Miyata, M., Omori, T. (2022). Estimating Children’s Intrinsic Motivation During a Class Based on Face Orientation and Gaze Information. In: Takama, Y., et al. Advances in Artificial Intelligence. JSAI 2021. Advances in Intelligent Systems and Computing, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-96451-1_12

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