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EduAction: A College Student Action Dataset for Classroom Attention Estimation

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14089))

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Abstract

With the development of the action recognition technique, it is possible to automatically analyze the college students’ attention status through the recognition of their behavior in the classroom, which is of great significance to the evaluation of teaching quality in class. In the consideration that the college student behavior dataset is scarce, we set up a novel college students’ action dataset in the classroom for attention estimation in this paper, named as EduAction dataset. The EduAction dataset consists of 7 types of actions and 718 action clips, collected in the real college classroom environment. Furthermore, an improved two-stream ConvNet is conducted on this dataset with the 5-fold cross-validation for the performance evaluation. The results of our benchmark model achieve an overall accuracy of 83.01%. This spontaneous dataset will be a great aid for the teaching quality analysis in the learning environment.

K. Liu and B. Chen—These authors contributed equally to this work.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No. 61772023), National Key Research and Development Program of China (No. 2019QY1803), and Fujian Science and Technology Plan Industry-University-Research Cooperation Project (No.2021H6015).

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Authors and Affiliations

  1. Xiamen University, Xiamen, Fujian, China

    Kunhong Liu, Bin Chen, Liyan Chen, Lu Lin, Fan Gao & Yudi Zhao

  2. Fujian University of Technology, Fuzhou, Fujian, China

    Yong Xu

Authors
  1. Kunhong Liu
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  2. Bin Chen
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  3. Liyan Chen
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  4. Yong Xu
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  5. Lu Lin
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  6. Fan Gao
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  7. Yudi Zhao
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Corresponding author

Correspondence to Liyan Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Liu, K. et al. (2023). EduAction: A College Student Action Dataset for Classroom Attention Estimation. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_20

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  • DOI: https://doi.org/10.1007/978-981-99-4752-2_20

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  • Print ISBN: 978-981-99-4751-5

  • Online ISBN: 978-981-99-4752-2

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