Emotional recognition and intervention of students in intelligent English teaching environment based on deep learning

Abstract

Background

With the advancement of the intelligent process in the field of education, emotional intelligence improves students’ learning effect and teaching interaction.

Objective

This study constructed an emotion recognition model, extracted the emotional characteristics of students in English learning, designed targeted emotional intervention strategies, optimized students’ emotional states, and promoted learning enthusiasm and participation.

Methods

Through the combination of data collection and sample selection, multi-channel data such as classroom observation records and student feedback are used to train and evaluate the emotion recognition model.

Results

The emotion recognition model based on deep learning performs well in both accuracy and efficiency, and can accurately identify different emotions generated by students in the learning process.

Conclusions

The implementation of emotional intervention strategies can effectively relieve students’ anxiety and pressure and improve their learning state. The complexity of emotional data and individual differences in intervention effects were also encountered in the research process. Future studies can further explore the aspects of data quality, model optimization and intervention paths to improve the accuracy and effect of emotion recognition and intervention.