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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JCER</journal-id><journal-title-group><journal-title>Journal of Contemporary Educational Research</journal-title></journal-title-group><issn>2208-8466</issn><eissn>2208-8474</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jcer.v8i11.8833</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Implementation of Digital Classroom State System for Teachers and Students Based on Large Models</title><url>https://artdesignp.com/journal/JCER/8/11/10.26689/jcer.v8i11.8833</url><author>LyuWenbo,ZhuGuangmin,QinZiyi,YanMengting,ZhangJie</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>11</issue><history><date date-type="pub"><published-time>2024-11-27</published-time></date></history><abstract>Deep learning has become a hot field of artificial intelligence, and the deep learning large model framework has become a bridgehead for the active layout of Chinese and foreign technology companies. Large models play a significant role in the application field, greatly improving the efficiency of training and optimization, and contributing to the landing of many innovative artificial intelligence tools. Based on the Chinese PaddlePaddle large model framework, an application system is designed in combination with the intelligent classroom teaching scenario, which uses machine vision algorithms to distinguish and present teachers’ and students’ behaviors, that is, the digitization and multi-classification scheme of class character states. After having digital data, data analysis can be carried out to evaluate the class status of teachers and students, and the traditional subjective judgment such as peacetime grades and teaching ability can be upgraded to the objective judgment of artificial intelligence.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Liu QT, He HY, Wu LJ, et al., 2019, Classroom Teaching Behavior Analysis Method Based on Artificial Intelligence and Application. China Audio-Visual Education, (09): 13–21.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Duan JL, 2018, Analysis and Evaluation System of Students’ Concentration in Class Based on Machine Vision, dissertation, Zhejiang Gongshang University, 3–15.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Jia PY, Zhang CH, Zhao XY, et al., 2019, Analysis of Classroom Student Status Based on Artificial Intelligence Video Processing. Modern Educational Technology, 29(12): 82–88.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Qin DY, 2019, Student Classroom Behavior Recognition Based on Deep Learning, dissertation, Central China Normal University, 9–12.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Xu JZ, Deng W, Wei YT, et al., 2020, Automatic Recognition of Student Classroom Behavior Based on Human Skeleton Information Extraction. Modern Educational Technology, (05): 108–113.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Wang P, 2020, Application Analysis and Design of Artificial Intelligence in Educational Videos. Journal of Audio-Visual Education Research, 41(03): 93–100 + 121.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Xia DX, Tian XY, Tang SN, et al., 2021, Analysis of Students’ Classroom Behavior Based on Visual Attention. Journal of Guizhou Normal University (Natural Science Edition), 39(04): 83–89 + 120.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Xiong JR, 2021, A Machine Vision Facial Expression Recognition and Detection System. Electronic Production, (04): 39, 40 + 38.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>Zhang K, 2021, Application Prospects of Artificial Intelligence Smart Classroom in the 5G Era. Post and Telecommunications Design Technology, (06): 84–87.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
