A database of students? spontaneous actions in the real classroom environment

Students' actions in the classroom play a key role in their studying performance in class. This study focuses on the automatic recognition of students' spontaneous actions in a real classroom environment. A database of students' spontaneous actions is first established in this study. Our database consists of 4917 images of students' spontaneous actions, including the actions of students in 10 learning states. Based on the characteristics of students' actions in the classroom, we proposed a new 11-layer convolutional neural network algorithm based on an enhanced deep super resolution network. In this algorithm, a data augmentation method is introduced for data processing. The experimental results show that the proposed algorithm effectively improved the accuracy of action recognition on the database established in this paper. Such a spontaneous database proposed in this paper provides good data support for the study of students' actions in the classroom.