Online English Teaching Quality Assessment Based on K-Means and Improved SSD Algorithm

Abstract

Classroom teaching quality is a key content to measure the teaching level, and the teaching effect can be intuitively reflected from the students' listening state. In order to improve the teaching quality, this paper proposes an online English teaching quality evaluation model based on K-means and an improved SSD algorithm. In the SSD algorithm, the backbone network is replaced by DenseNet with a dense connection to improve detection accuracy. The network structure of quadratic regression is designed to solve the problem of unbalance between positive and negative samples in the default box of the candidate region. A feature graph scaling method is used to fuse feature graphs without introducing additional parameters. The number of default boxes and the optimal aspect ratio were obtained by k-means clustering analysis. Finally, the state of students in the teaching process is predicted through the dual-mode recognition model of facial expression and posture, and the state of students in class is judged. Experimental comparison and analysis were conducted on the public data set and a self-built classroom teaching video data set. Experiments show that compared with other comparison algorithms, the algorithm presented in this paper performs better in the index of detection accuracy.