Students' Health Emotions Management Driven English Teaching Quality Evaluation

Abstract

Teachers are unable to timely and effectively perceive the emotional states of many students in the learning process due to the large number of classes, which leads to that these learners with cognitive impairment of new knowledge keep at the negative emotional state and has no high-learning efficiency. In particular, English is an international language, of which these students are very subjected to the health emotion management. Therefore, it is very necessary to investigate the students’ health emotions and promote the improvement of English teaching quality, where the basis idea is to collect the facial expressions of students during the teaching process due to the fact that the facial expressions can reflect students’ psychological state and emotional changes. Consider that the whole collection process is dynamic and the involve data on the facial expressions is large-scale, and thus it needs an intelligent methods to analyze such large-scale heath management data. This paper proposes a facial expression analysis strategy based on deep multi-kernel learning, and the experimental results show that the recognition accuracy basic facial expressions can reach 94.75%. In other words, the students’ health emotions can be managed efficiently and the proposed health analysis method can provide a significant and universal reference for the English teaching in future.