An analysis of English classroom behavior by intelligent image recognition in IoT

In order to strengthen the management of English classroom discipline and improve the efficiency of students’ English classroom learning, students’ English classroom behavior based on intelligent image recognition is analyzed in IoT (Internet of things). The working scenes and practical significance of deep learning and IoT are analyzed and then the effects of four models on students' behavior analysis in English classroom are discussed. The results show that the classroom behavior analysis model proposed is feasible. The recognition system judges whether the students are listening seriously from three aspects, namely students' side face, head up and down, and their eyelid opening. The comparison of the four models of VGG16, ResNet18, ResNet50 and AlexNet shows that the accurate recognition rate of VGG16 for students' behavior in English classroom reaches 94.15%. Experiments show that the method provides a more objective evaluation of students’ classroom behavior. As a whole, students’ classroom behavior analysis based on IIRT (intelligent image recognition technology) in IOT is practicable for improving English classroom efficiency.