Abstract
Teachers’ classroom behavior testing can help the understand the learning situation of students, feedback the teaching effect, and formulate corresponding measures to help. Traditional evaluation methods rely on manual testing, which is time-consuming and lacks objectivity. This article suggests a new approach to analyze the behavior of teachers by using the human skeleton posture. This method first uses the popular human posture gesture estimation technology to obtain the skeleton joint node of the teacher in the teaching process, and then remove several of the joint nodes that have a weaker effect on the classification of teachers in class. Finally, a convolutional neural network was designed to classify the teacher’s behavior based on the skeleton joint nodes and confidence obtained from processing the input. The experimental results show that the effectiveness of the method helps the dynamic management and evaluation of classroom teachers’ behavior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brophy, J.E.: Teacher behavior and its effects. J. Educ. Psychol. 71(6), 733 (1979)
Lin, J., Jiang, F., Shen, R.: Hand-raising gesture detection in real classroom. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6453–6457. IEEE (2018)
Shao, B., Jiang, F., Shen, R.: Multi-object detection based on deep learning in real classrooms. In: Geng, X., Kang, B.-H. (eds.) PRICAI 2018: Trends in Artificial Intelligence: 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, 28–31 Aug 2018, Proceedings, Part II, pp. 352–359. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-97310-4_40
Burroughs, N., Gardner, J., Lee, Y., et al.: A review of the literature on teacher effectiveness and student outcomes. In: Burroughs, N., et al. (eds.) Teaching for Excellence and Equity: Analyzing Teacher Characteristics, Behaviors and Student Outcomes with TIMSS, pp. 7–17. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-16151-4_2
Agbo, G.C., Agbo, P.A.: The role of computer vision in the development of knowledge-based systems for teaching and learning of English language education. ACCENTS Trans. Image Process. Comput. Vis. 6(19), 42 (2020)
Kaplan, S., Guvensan, M.A., Yavuz, A.G., et al.: Driver behavior analysis for safe driving: a survey. IEEE Trans. Intell. Transp. Syst. 16(6), 3017–3032 (2015)
Shirazi, M.S., Morris, B.T.: Vision-based pedestrian behavior analysis at intersections. J. Electron. Imaging 25(5), 051203 (2016)
Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43, 1–54 (2015)
Flanders N A. Analyzing teaching behavior (1970)
Khan, N.U., Wan, W.: A review of human pose estimation from single image. In: 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp. 230–236. IEEE (2018)
Sun, K., Xiao, B., Liu, D., et al.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)
Ren, S., He, K., Girshick, R., et al.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, 28 (2015)
Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, 6–12 Sept 2014, Proceedings, Part V 13, pp. 740–755. Springer International Publishing (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Zhao, W., Wang, M., Liu, Y., et al.: Generalizable crowd counting via diverse context style learning. IEEE Trans. Circuits Syst. Video Technol. 32(8), 5399–5410 (2022)
Xu, X., Song, J., Lu, H., et al.: Modal-adversarial semantic learning network for extendable cross-modal retrieval. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 46–54 (2018)
Acknowledgement
This work is supported in part by the National Nature Science Foundation of China (No. 61931012), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20210594), in part by the Natural Science Foundation for Colleges and Universities in Jiangsu Province (Grant No. 21KJB520016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Hu, H., Gao, H. (2024). Teacher Classroom Behavior Detection Based on a Human Pose Estimation Algorithm. In: Lu, H., Cai, J. (eds) Artificial Intelligence and Robotics. ISAIR 2023. Communications in Computer and Information Science, vol 1998. Springer, Singapore. https://doi.org/10.1007/978-981-99-9109-9_7
Download citation
DOI: https://doi.org/10.1007/978-981-99-9109-9_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9108-2
Online ISBN: 978-981-99-9109-9
eBook Packages: Computer ScienceComputer Science (R0)