Title: A method for detecting students' concentration in online Chinese course learning based on deep learning

Authors: Hang Liu

Addresses: School of Humanities, Puyang Vocational and Technical College, Puyang, 457000, China

Abstract: There is a problem of poor detection effect in detecting student concentration during online Chinese course learning. Therefore, a deep learning-based method for detecting student concentration in online Chinese courses is designed in this paper. Firstly, the facial expression feature attributes of Chinese online learning students are determined, and the Gabor + LBP method is used to extract their facial expression features. Then, the AlphaPose algorithm is used to obtain the joint coordinates of Chinese online learning students and extract specific action detail features. Finally, convolutional blocks are introduced for student focus feature vector classification, and a deep learning-based focus detection model is constructed to output the detection results. The experimental results show that this proposed approach improves the accuracy of identifying students' attentiveness in remote Mandarin learning. This method helps to more accurately grasp students' learning status, thus providing strong support for optimising online Chinese course teaching strategies and improving teaching quality.

Keywords: deep learning; Chinese courses; online learning; student focus; testing; spatial attention; fully connected layer.

DOI: 10.1504/IJCEELL.2026.152126

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.1/2, pp.1 - 18

Received: 23 Oct 2024
Accepted: 28 Aug 2025
Published online: 09 Mar 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article