Multi-Feature Fusion for E-Learning Based Student Concentration Analysis
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Since the outbreak of COVID-19, the hybrid teaching style, combining online and offline methods, has evolvedinto a normal pedagogical approach. In offline classrooms, teachers may pay attention to the state of studentsand observe whether they are listening attentively, to adjust the teaching process in time. However, in the Elearningenvironment, teachers are hindered by their inability to access students’ states in time. Particularly, itis challenging to find out whether students are distracted in class. Although there are various existing studentconcentration analysis models, the metrics, such as convenience and accuracy, of these models often fail tomeet 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 collectedand seven factor features were summarized. To establish the experimental dataset, 2,000 video clips wereacquired, 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. Experimentalresults demonstrated that the proposed method achieves 0.88 accuracy, outperforming the existing state-of-theartconcentration analysis methods. The proposed method is designed to detect distracted students and toprovide reference for teachers to adjust E-learning arrangements, which is of great application value.
Abstract
Since the outbreak of COVID-19, the hybrid teaching style, combining online and offline methods, has evolvedinto a normal pedagogical approach. In offline classrooms, teachers may pay attention to the state of studentsand observe whether they are listening attentively, to adjust the teaching process in time. However, in the Elearningenvironment, teachers are hindered by their inability to access students’ states in time. Particularly, itis challenging to find out whether students are distracted in class. Although there are various existing studentconcentration analysis models, the metrics, such as convenience and accuracy, of these models often fail tomeet 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 collectedand seven factor features were summarized. To establish the experimental dataset, 2,000 video clips wereacquired, 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. Experimentalresults demonstrated that the proposed method achieves 0.88 accuracy, outperforming the existing state-of-theartconcentration analysis methods. The proposed method is designed to detect distracted students and toprovide reference for teachers to adjust E-learning arrangements, which is of great application value.