Machine Learning-Based Student Emotion Recognition for Business English Class

Abstract

Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.

Research on Classroom Emotion Recognition Algorithm Based on Visual Emotion Classification

“…Since 2000, the integration of computer sentiment analysis and educational teaching has been greatly promoted. Researchers have used video analysis to understand learners′ emotional states, providing teachers with timely feedback so that they can revise course content, adjust course difficulty, select teaching methods, and control the teaching schedule, greatly promoting changes in the teaching model and improving teaching quality [ 16 ]. The biggest problem of emotion recognition research in a classroom teaching environment is the need to detect the unique emotion category for the unique environment, there is a serious lack of publicly available emotion datasets for the classroom environment, and due to the large change in students′ posture and orientation in the classroom environment, the existence of such actions as lowering the head and turning sideways will lead to incomplete face pictures and coupled with the influence of background, illumination, and occlusion, the available face images are less.…”

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

“…Moreover, Cui et al [62] proposed an emotion recognition model that monitors each student's real-time emotional state during English learning. For example, when frustration or boredom is detected, machine learning will switch to contents that interest the student or are easier to learn, keeping the student engaged in learning.…”

L2 Learner Cognitive Psychological Factors About Artificial Intelligence Writing Corrective Feedback

“…Emotion, an influential factor in learning, is also studied by using AI technology. The emotion recognition model proposed by Cui & Wang (2021) can detect and monitor the learner emotional states. Upon detecting frustration or boredom, the AI system will make timely adjustment to what interest the learners or what are easier for them to learn to keep them in an active state of learning.…”