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Affected by the epidemic, online teaching has been implemented all over the country, forming an education model of \u201cdual integration\u201d of online and offline teaching. However, the disadvantages of online teaching are also very obvious; that is, teachers cannot understand the students\u2019 listening status in real-time. Therefore, our study adopts automatic face detection and expression recognition based on a deep learning framework and other related technologies to solve this problem, and it designs an analysis system of students\u2019 class concentration based on expression recognition. The students\u2019 class concentration analysis system can help teachers detect students\u2019 class concentration and improve the efficiency of class evaluation. In this system, OpenCV is used to call the camera to collect the students\u2019 listening status in real-time, and the MTCNN algorithm is used to detect the face of the video to frame the location of the student\u2019s face image. Finally, the obtained face image is used for real-time expression recognition by using the VGG16 network added with ECANet, and the students\u2019 emotions in class are obtained. The experimental results show that the method in our study can more accurately identify students\u2019 emotions in class and carry out a teaching effect evaluation, which has certain application value in intelligent education fields, such as the smart classroom and distance learning. For example, a teaching evaluation module can be added to the teaching software, and teachers can know the listening emotions of each student in class while lecturing.<\/jats:p>","DOI":"10.3390\/fi14060177","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:22:39Z","timestamp":1654820559000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Evaluation of Online Teaching Quality Based on Facial Expression Recognition"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6421-3481","authenticated-orcid":false,"given":"Changbo","family":"Hou","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Ai","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1379-9301","authenticated-orcid":false,"given":"Yun","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Q., Liu, X., Gong, X., and Jing, S. 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