{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:20:30Z","timestamp":1770150030666,"version":"3.49.0"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Educational Science Planning","award":["CDDB19237"],"award-info":[{"award-number":["CDDB19237"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,10,20]]},"abstract":"<jats:p>To improve classroom teaching behavior recognition and evaluation accuracy, this paper proposes a new model based on deep learning. First, we obtain the classroom teaching behavior characteristic data through the SVM\u2019s linear separable initial and determine the relationship of the characteristic sample data in the hyperplane. Then, we obtain the heterogeneous support vector of the online learning behavior characteristic sample data in the SVM\u2019s hyperplane and complete the extraction of data with the help of convolutional neural networks. We then use a decision matrix to analyze the hierarchical process, determine the weight of classroom teaching behavior indicators, verify their consistency, and complete the evaluation by calculating the membership of evaluation factors. The experimental results show that the identification and evaluation method of classroom teaching behavior in this paper can effectively improve the identification accuracy of the classroom teaching behavior.<\/jats:p>","DOI":"10.1155\/2021\/6336773","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:20:32Z","timestamp":1634764832000},"page":"1-8","source":"Crossref","is-referenced-by-count":19,"title":["A Convolutional Neural Network (CNN) Based Approach for the Recognition and Evaluation of Classroom Teaching Behavior"],"prefix":"10.1155","volume":"2021","author":[{"given":"Guang","family":"Li","sequence":"first","affiliation":[{"name":"Institue of Data Science, City University of Macau, Macau, China"}]},{"given":"Fangfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Capital Normal University High School, Beijing 100089, China"}]},{"given":"Yuping","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Haidian Experimental Middle School, Beijing 100089, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2724-2926","authenticated-orcid":true,"given":"Yongde","family":"Guo","sequence":"additional","affiliation":[{"name":"Institue of Data Science, City University of Macau, Macau, China"}]},{"given":"Liang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Mathematics and Data Science in the School of Information Technology, Macao University of Science and Technology, Macau, China"}]},{"given":"Linkai","family":"Zhu","sequence":"additional","affiliation":[{"name":"Trusted Computing and Information Assurance Laboratory, Institute of Software Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Institue of Data Science, City University of Macau, Macau, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.31863\/jse.2019.12.35.3.27"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1002\/tea.21548"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s10935-018-00532-1"},{"issue":"2","key":"4","article-title":"Activity systems analysis of classroom teaching and learning of mathematics: a case study of Japanese secondary schools","volume":"5","author":"Y. 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