Analysis of Classroom Teaching Status Based on Target Detection Model
Biao Wang, Xiao Juan Guo · 2019
The research and evaluation of classroom teaching quality has always been an important concern of education. Traditional classroom teaching quality evaluation mainly depends on human subjective judgment.It is time-consuming, laborious and inaccurate to some extent.The rapid development of the Internet has brought new progress to the wisdom classroom. This paper aims at the classroom teacher's teaching action recognition,we use the deep convolutional neural network to build the target detection model, classifies the behavioral actions of the classroom teaching.It is more objectively for judging the teaching quality and more convenient for making appropriate improvements. The model consists of an input layer, a two-layer convolutional layer, a two-layer pooling layer, a Batch Normalization layer, and an output layer, which can effectively extract data features and achieve better classification and recognition effects.