Improved ECA-ResTCN for Online Classroom Student Attention Recognition

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...

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Bibliographic Details
Main Authors: Qun Tu, Xiaoru Zhao, Daqing Gong, Qianqian Zhang
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:
attention mechanism
convolutional neural network
convolutional temporal network
student concentration
Online Access:https://hrcak.srce.hr/file/457163

Internet

https://hrcak.srce.hr/file/457163

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