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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.v5i12.2855</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Evaluation of Classroom Teaching Effect Based on Facial Expression Recognition</title><url>https://artdesignp.com/journal/JCER/5/12/10.26689/jcer.v5i12.2855</url><author>MaoJun</author><pub-date pub-type="publication-year"><year>2021</year></pub-date><volume>5</volume><issue>12</issue><history><date date-type="pub"><published-time>2021-12-23</published-time></date></history><abstract>Classroom is an important environment for communication in teaching events. Therefore, both school and society should pay more attention to it. However, in the traditional teaching classroom, there is actually a relatively lack of communication and exchanges. Facial expression recognition is a branch of facial recognition technology with high precision. Even in large teaching scenes, it can capture the changes of students’ facial expressions and analyze their concentration accurately. This paper expounds the concept of this technology, and studies the evaluation of classroom teaching effects based on facial expression recognition.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Liao E, Li W, Cai X, 2020, Research on Big Data Analysis and Teaching Decision of Visual Behavior Recognition. Engineering and Technological Research, 5(12): 233-235.</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>Chen Z, Zhu X, 2019, Automatic Recognition of Learners’ Emotions Based on Facial Expressions: Relevance, Status, Existing Problems and Improvement Path. Journal of Distance Education, 37(04): 64-72.</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>Chen S, Dai J, Gao X, et al., 2019, Research on Dynamic Emotion Recognition of Students in Classroom Teaching. The Chinese Journal of ICT in Education, 2019(13): 33-36.</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>He X, Gao Q, Li Y, et al., 2019, Research on Spontaneous Learning Facial Expression Recognition Based on Deep Learning Model. Computer Applications and Software, 36 (03): 180-186.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
