Abstract
In the case of the difficulty of teachers to know the learning status of students in the online classroom, this paper mainly proposes a learning status feedback method based on facial expression recognition. With the students’ images captured on class, the OpenCV facial detection is used to extract the facial information, which will be classified into seven emotion categories through the ResNetl8 convolutional neural network model. Through the PAD emotion model, the seven types of emotions are mapped to the three dimensions of the PAD emotion space, and the mapping values are used to calculate the students’ learning engagement with the formula, so that it can numerically quantify the abstract learning emotion status. Based on statistical principles, the learning engagement is refined to three characteristics - level, volatility, and persistence. Taking an online physics class divided into different teaching sessions as an example, this paper analyzes the study status of students with PAD model and provides feedback, which confirms the feasibility of using facial emotion recognition technology in online classroom status. In this way, teachers can easily judge students’ learning engagement in online classroom and adjust their teaching strategies to improve the quality of online classroom teaching.
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