Improved ECA-ResTCN for Online Classroom Student Attention Recognition
With the rapid rise of online classrooms, monitoring student engagement is critical but challenging for educators. This work explores how artificial intelligence (AI) and big data techniques can automatically evaluate student concentration levels in online courses. We developed an end-to-end ResTCN...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2024-01-01
|
| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/457163 |
| _version_ | 1797193676243337216 |
|---|---|
| author | Qun Tu Xiaoru Zhao Daqing Gong Qianqian Zhang |
| author_facet | Qun Tu Xiaoru Zhao Daqing Gong Qianqian Zhang |
| author_sort | Qun Tu |
| collection | DOAJ |
| description | With the rapid rise of online classrooms, monitoring student engagement is critical but challenging for educators. This work explores how artificial intelligence (AI) and big data techniques can automatically evaluate student concentration levels in online courses. We developed an end-to-end ResTCN model combining ResNet and temporal convolutional networks (TCN) to extract spatial and temporal video features. Further, we introduced a CutMix data augmentation method and an efficient channel attention (ECA) module to enhance model training. Evaluated on a public dataset of student videos, our approach achieved 63.28% accuracy in classifying student engagement, outperforming state-of-the-art methods. The contributions are a novel spatiotemporal neural architecture, data augmentation strategy, and attention mechanism tailored for the student engagement recognition task. This demonstrates the potential of AI in creating smart education systems. |
| first_indexed | 2024-04-24T05:44:10Z |
| format | Article |
| id | doaj.art-e33d3d81eaed4c9395ce63a22989e4da |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T05:44:10Z |
| publishDate | 2024-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-e33d3d81eaed4c9395ce63a22989e4da2024-04-23T19:03:21ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392024-01-0131383283610.17559/TV-20231013001024Improved ECA-ResTCN for Online Classroom Student Attention RecognitionQun Tu0Xiaoru Zhao1Daqing Gong2Qianqian Zhang3School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China No.15, Beisanhuandong Road, Chaoyang District, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, China No. 3, Shangyuancun, Haidian District, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, China No. 3, Shangyuancun, Haidian District, Beijing, China E-mail:School of Information, Beijing Wuzi University, Beijing 101149, China No. 1, Fuhe Street, Tongzhou District, Beijing, ChinaWith the rapid rise of online classrooms, monitoring student engagement is critical but challenging for educators. This work explores how artificial intelligence (AI) and big data techniques can automatically evaluate student concentration levels in online courses. We developed an end-to-end ResTCN model combining ResNet and temporal convolutional networks (TCN) to extract spatial and temporal video features. Further, we introduced a CutMix data augmentation method and an efficient channel attention (ECA) module to enhance model training. Evaluated on a public dataset of student videos, our approach achieved 63.28% accuracy in classifying student engagement, outperforming state-of-the-art methods. The contributions are a novel spatiotemporal neural architecture, data augmentation strategy, and attention mechanism tailored for the student engagement recognition task. This demonstrates the potential of AI in creating smart education systems.https://hrcak.srce.hr/file/457163attention mechanismconvolutional neural networkconvolutional temporal networkstudent concentration |
| spellingShingle | Qun Tu Xiaoru Zhao Daqing Gong Qianqian Zhang Improved ECA-ResTCN for Online Classroom Student Attention Recognition Tehnički Vjesnik attention mechanism convolutional neural network convolutional temporal network student concentration |
| title | Improved ECA-ResTCN for Online Classroom Student Attention Recognition |
| title_full | Improved ECA-ResTCN for Online Classroom Student Attention Recognition |
| title_fullStr | Improved ECA-ResTCN for Online Classroom Student Attention Recognition |
| title_full_unstemmed | Improved ECA-ResTCN for Online Classroom Student Attention Recognition |
| title_short | Improved ECA-ResTCN for Online Classroom Student Attention Recognition |
| title_sort | improved eca restcn for online classroom student attention recognition |
| topic | attention mechanism convolutional neural network convolutional temporal network student concentration |
| url | https://hrcak.srce.hr/file/457163 |
| work_keys_str_mv | AT quntu improvedecarestcnforonlineclassroomstudentattentionrecognition AT xiaoruzhao improvedecarestcnforonlineclassroomstudentattentionrecognition AT daqinggong improvedecarestcnforonlineclassroomstudentattentionrecognition AT qianqianzhang improvedecarestcnforonlineclassroomstudentattentionrecognition |