Computer vision recognition in the teaching classroom: A Review
DOI:
https://doi.org/10.4108/airo.4079Keywords:
Artificial Intelligence, Deep Learning, Computer Vision, Teaching classroom behaviour recognitionAbstract
Artificial intelligence introduces computer vision recognition into the teaching classroom, and computer vision recognition technology lays a solid foundation for the intelligent teaching classroom. Through the classroom camera video stream to the classroom student information data collection, voice, posture, facial, physiological signal data recognition analysis processing to extract and define the characteristics of student behaviour, automatic classification behaviour and then record and display student behaviour, thus effectively help teachers to grasp the students learning state and emotions, to promote the quality of teaching has far-reaching significance.
Downloads
References
Jabbar M A, Kantipudi M V V P and Peng S L, "Machine Learning Methods for Signal," Image and Speech Processing, CRC Press, 2022.
Tian Y, "Artificial intelligence image recognition method based on convolutional neural network algorithm," IEEE Access, vol. 8, pp. 125731-125744, 2020.
Yang M, Kumar P and Bhola J, "Development of image recognition software based on artificial intelligence algorithm for the efficient sorting of apple fruit," International Journal of System Assurance Engineering and Management, pp. 1-9, 2021.
Chen H, Geng L and Zhao H, "Image recognition algorithm based on artificial intelligence," Neural Computing and Applications, pp. 1-12, 2021.
Feng H H and Zou B, "Exploring and Reflecting on Student Motivation Enhancement in Traditional Classrooms," Journal of Higher Education, vol. 7, no. 25, pp. 56-59, 2021.
Zhang J X, Zhu L and Wu Z F, "Classroom Immediate Assessment Based on Information Technology," Journal of Yangzhou University (Higher Education Research Edition), vol. 22, no. 2, pp. 80-85, 2019.
Nash B L, "Constructing meaning online: Teaching critical reading in a post‐truth era," The Reading Teacher, vol. 74, no. 6, pp. 713-722, 2021.
Malin H, "Teaching for purpose: Preparing students for lives of meaning," Harvard Education Press, 2021.
Munna A S and Kalam M A. "Teaching and learning process to enhance teaching effectiveness: a literature review," International Journal of Humanities and Innovation (IJHI), vol. 4,no. 1, pp. 1-4, 2021.
Shao Y C, Li C D and Cao Y, "Intelligent Analysis of Classroom Behavior in Teaching Reform," Teaching and Management, 2021(15): 29-33.
Chiu Y C, Tsai C Y and Ruan M D, Mobilenet-SSDv2: "An improved object detection model for embedded systems", 2020 International Conference on system science and Engineering (ICSSE), 2020: IEEE, pp. 1-5.
Zhang W, Xu Q, "Optimization of college English classroom teaching efficiency by deep learning SDD algorithm," Computational Intelligence and Neuroscience, 2022, 2022.
Wang G H, Zhang X and Zheng H, "Analysis of students' classroom learning status based on deep learning," Journal of Higher Education, vol. 8, no. 31, pp. 1-5, 2022.
Rosati R, Romeo L and Silvestri S, "Faster R-CNN approach for detection and quantification of DNA damage in comet assay images," Computers in Biology and Medicine, 2020, 123: 103912.
L Yuan, "Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN," Wireless Communications and Mobile Computing, 2022, 2022.
Xu J Z, Deng W and Wei Y T, "Automatic Recognition of Student’s Classroom Behaviors based on Human Skeleton Information Extraction," Modern Education Technology, vol. 30, no. 5, pp. 108-113, 2020.
He X L, Yang F and Chen Z Z, "The Recognition of Student Classroom Behavior based on Human Skeleton and Deep Learning," Modern Education Technology, vol. 30, no. 11, pp. 105-112, 2020.
Liao P, Liu C M and Su H, "A deep learning-based system for detecting and analyzing abnormal student behavior in the classroom," Electronic World, 2018(08): 97-98.
Jiang X Y, Zhang Z W and Tan S Q, "Student classroom behavior identification based on residual network," Modern Computers, 2019(20): 23-27.
Liu X Y, Ye S P and Zhang D H, "Improved multi-objective regression student classroom action detection method," Computer Engineering and Design, vol. 41, no. 9, pp. 2684-2689, 2020.
Yang B, Song X N and Feng Z Q," Gesture Recognition in Complex Background Based on Distribution Features of Hand," Journal of Computer-Aided Design ＆ Computer Graphics, vol. 22, no. 10, pp. 1841-1848, 2010.
Pantic M., Rothkrantz and L.J.M.,"Facial Action Recognition for Facial Expression Analysis From Static Face Images ," IEEE transactions on systems, man, and cybernetics, Part B.Cybernetics, vol. 34, no. 3, pp. 1449-1461, 2004.
Ding R, Song G D and Lin X G, "Comparison of Eigenface and Elastic Matching in Human Face Recognition," Computer Engineering and Applications, 2002(07): 1-2+19.
Jia L Y, Zhang C H and Zhao X Y, "Analysis of Students Status in Class Based on Artificial Intelligence and Video Processing," Modern Education Technology, vol. 29, no. 12, pp. 82-88, 2019.
Ma X L, Guo S N and Wu Y H, "The Recognition and Application of Educational Emotion Based on Image Analysis," Modern Education Technology, vol. 30, no.2, pp. 118-121, 2020.
Han L, Li Y and Zhou Z J," Research on the Relationship of Critical Thinking and Community of Inquiry Model," Modern Distance Education Research, 2017(04): 97-103+112.
Iain R.Murray and John L.Arnott,"Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion ,"The Journal of the Acoustical Society of America,vol. 93,no. 2, pp. 1097-108, 1993.
Wang H, Guo H and Zhang K, "Automatic sleep staging method of EEG signal based on transfer learning and fusion network," Neurocomputing, 2022, 488: 183-193.
Hemanth D J, "EEG signal based modified Kohonen neural networks for classification of human mental emotions,"Journal of Artificial Intelligence and Systems, vol. 2,no. 1,pp. 1-13, 2020.
Zhou J G, Tang D M and Peng Z," Students' Expression Analysis in the Classroom based on Gradient Boosting Decision Tree and Convolution Neural Network," Journal of Chengdu University of Information Engineering, vol. 32, no. 5, pp. 508-512, 2017.
Zhou P X, Deng W and Guo P Y," Research on Intelligent Recognition of S-T Behavior in Classroom Teaching Video," Modern Education Technology, vol. 28, no. 6, pp. 54-59, 2018.
Gong w, "Design and Implementation of Student Learning Behavior Recognition System Based on Key Points Detection of Bones," Jilin University, 2019.
Hinton G E and Salakhutdinov R R, "Reducing the dimensionality of data with neural networks," Science，2006，313 ( 5786) : 504－507．
Jermsittiparsert K, Abdurrahman A and Siriattakul P, "Pattern recognition and features selection for speech emotion recognition model using deep learning," International Journal of Speech Technology, vol. 23, pp.799-806, 2020.
Zhang Yiwen， Wu Zhe and Chen Xianjin， "Classroom behavior recognition based on improved yolov3", 2020 International Conference on Artificial Intelligence and Education ( ICAIE) , 2020: IEEE，pp. 93－97．
Hou C K, "Research on Automatic Recognition of classroom teacher behavior based on multimodal fusion," Huazhong Normal University, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hui Jiang, Wentao Fu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
