A Method for Identifying Abnormal Behaviors in College English Smart Classroom Teaching Based on Deep Learning

The acquisition of behavior recognition in college English smart classroom teaching helps to achieve real-time monitoring and alerting of student behavior, providing important guarantees for improving the quality and effectiveness of college English smart classroom teaching. To this end, a method for identifying abnormal behavior in college English smart classroom teaching based on deep learning is proposed. On the basis of obtaining a large amount of data on teaching behavior in college English smart classrooms, various abnormal teaching behavior characteristics such as playing with mobile phones, watching screens, whispering, and distraction were extracted and integrated from it. The Convolutional neural network in the deep learning algorithm is used to train and recognize these features, so as to achieve the purpose of identifying abnormal teaching behaviors. The experimental results show that this method can fully utilize deep learning methods to achieve recognition of teaching abnormal behavior, with high recognition accuracy, short time consumption, and high recognition accuracy. It helps teachers discover and solve abnormal situations in teaching in a timely manner, improving teaching effectiveness and students’ learning quality.