Changjian Zhou, He Jia, Jinge Xing, and Yunfu Liang, Journal of Information Processing Systems Vol. 21, No. 2, pp. 114-124, Apr. 2025  

https://doi.org/10.3745/JIPS.04.0339
Keywords: Artificial intelligence, Concentration Analysis, educational technology, Multi-Feature Fusion, online learning
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Abstract

Since the outbreak of COVID-19, the hybrid teaching style, combining online and offline methods, has evolved into a normal pedagogical approach. In offline classrooms, teachers may pay attention to the state of students and observe whether they are listening attentively, to adjust the teaching process in time. However, in the Elearning environment, teachers are hindered by their inability to access students’ states in time. Particularly, it is challenging to find out whether students are distracted in class. Although there are various existing student concentration analysis models, the metrics, such as convenience and accuracy, of these models often fail to meet the expected requirements. To address these obstacles, a multi-feature fusion method is proposed for Elearning-based student concentration analysis in this work. In this study the 300 questionnaires were collected and seven factor features were summarized. To establish the experimental dataset, 2,000 video clips were acquired, and each one was labelled with one of the five-class concentration degree scores. Finally, the ResNet-50 deep learning model with multilayer perceptron layer was employed for training and fine-tuning. Experimental results demonstrated that the proposed method achieves 0.88 accuracy, outperforming the existing state-of-theart concentration analysis methods. The proposed method is designed to detect distracted students and to provide reference for teachers to adjust E-learning arrangements, which is of great application value.

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