Abstract
In the classroom, the behavior of students can directly reflect the teaching quality of teachers. Based on the deep learning model, the analysis and evaluation of classroom behavior can help promote the intelligent evaluation of teaching and improve the teaching quality. The research on the detection of students' behavior in the classroom which is based on deep convolution neural network , has made great progress, mainly focusing on the classification of classroom behavior and the accuracy of detection, but rarely involving the evaluation between learners, teaching quality and detection results. According to the actual needs of teaching quality evaluation, this paper proposes an individual evaluation method for students and a method to evaluate teaching quality in the class, based on the application of deep convolution neural network in target detection and student classroom behavior recognition. This paper uses ResNet50 pre-training model to conduct parameter learning and fine-tuning on the classroom scene behavior recognition data set. The results show that ResNet50 can optimize the parameters faster than other pre-training models, and can effectively identify students' classroom behavior. By calculating the test results based on the evaluation model, the quantitative evaluation of students’ behavior and teaching quality can be implemented.
Published Version
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