Study on the construction of learners’ emotion perception model and its improvement of behavioral patterns in artificial intelligence-assisted education based on deep learning

With the wide application of deep learning in the field of education, student emotion perception has become one of the research hotspots. The study recognizes learners’ facial expressions by face detection algorithm after collecting learners’ data and preprocessing. Algorithms such as convolutional neural network and ConvLSTM are used to recognize learners’ emotions, and learners’ emotions are constructed to be modeled. Evaluate the learner emotion performance of this paper’s model and compare it with other emotion recognition models. The model of this paper is used for practical research to collect students’ emotions in six classes, and statistics and analysis are performed. Finally, by studying the relationship between students’ emotions and behaviors, targeted suggestions for improving students’ behaviors are proposed. The accuracy of this paper’s model in recognizing student emotions on the RAF-DB dataset and classroom dataset is 90.32% and 97.65%, respectively, which is much higher than that of other pre-trained models. The recognition accuracy of this paper’s model for eight types of student emotions is between [0.93, 0.98]. In the statistics of classroom students’ emotions, the main emotions of students in session 1 were concentration, in session 2 were surprise and concentration, in sessions 3, 4, and 6 were surprise and delight, and in session 5 were concentration and disappointment. Focus was significantly positively correlated with “serious attendance”, “thinking”, “answering questions”, “discussing” and “doing tests”, tiredness was significantly positively correlated with “answering questions”, “reviewing” and “deserting”, boredom was significantly positively correlated with “answering questions”, “doing quizzes”, “reviewing” and “desertion”, doubts were significantly positively correlated with “discussing”, “doing quizzes” and “reviewing”, distraction was significantly positively correlated with “reviewing” and “desertion”, happiness was significantly positively correlated with “discussion”, and disappointment was significantly positively correlated with “desertion”.