Original scientific paper

https://doi.org/10.17559/TV-20231013001024

Improved ECA-ResTCN for Online Classroom Student Attention Recognition

Qun Tu ; School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China No.15, Beisanhuandong Road, Chaoyang District, Beijing, China
Xiaoru Zhao ; School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China No. 3, Shangyuancun, Haidian District, Beijing, China
Daqing Gong ; School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China No. 3, Shangyuancun, Haidian District, Beijing, China E-mail:
Qianqian Zhang ; School of Information, Beijing Wuzi University, Beijing 101149, China No. 1, Fuhe Street, Tongzhou District, Beijing, China

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.

Keywords

attention mechanism; convolutional neural network; convolutional temporal network; student concentration

Hrčak ID:

316365

URI

https://hrcak.srce.hr/316365

Publication date:

23.4.2024.

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