用于在线课堂学生注意力识别的改进型 ECA-ResTCN

TU Qun, Xiaoru Zhao, Daqing Gong, Qianqian Zhang
{"title":"用于在线课堂学生注意力识别的改进型 ECA-ResTCN","authors":"TU Qun, Xiaoru Zhao, Daqing Gong, Qianqian Zhang","doi":"10.17559/tv-20231013001024","DOIUrl":null,"url":null,"abstract":": 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.","PeriodicalId":510054,"journal":{"name":"Tehnicki vjesnik - Technical Gazette","volume":"10 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved ECA-ResTCN for Online Classroom Student Attention Recognition\",\"authors\":\"TU Qun, Xiaoru Zhao, Daqing Gong, Qianqian Zhang\",\"doi\":\"10.17559/tv-20231013001024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": 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.\",\"PeriodicalId\":510054,\"journal\":{\"name\":\"Tehnicki vjesnik - Technical Gazette\",\"volume\":\"10 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tehnicki vjesnik - Technical Gazette\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17559/tv-20231013001024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tehnicki vjesnik - Technical Gazette","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17559/tv-20231013001024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

:随着在线课堂的迅速兴起,监控学生的参与度对教育工作者来说至关重要,但也极具挑战性。这项工作探讨了人工智能(AI)和大数据技术如何在在线课程中自动评估学生的专注程度。我们开发了一个端到端的 ResTCN 模型,结合 ResNet 和时序卷积网络 (TCN),以提取空间和时间视频特征。此外,我们还引入了 CutMix 数据增强方法和高效通道注意(ECA)模块,以增强模型训练。在学生视频的公共数据集上进行评估后,我们的方法在学生参与度分类方面达到了 63.28% 的准确率,优于最先进的方法。我们的贡献在于针对学生参与度识别任务定制了新颖的时空神经架构、数据增强策略和注意力机制。这证明了人工智能在创建智能教育系统方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信