Design and Implementation of English Teaching Emotional Feedback System Based in Machine Learning

Abstract

This study pioneers a novel approach aimed at enriching English language education by introducing an Emotional Feedback System driven by Machine Learning (ML). By harnessing advanced techniques in Natural Language Processing (NLP) and Computer Vision, the system operates in real-time to interpret students' emotional responses. This capability enables the system to deliver tailored feedback designed to enhance engagement and optimize learning outcomes. Rigorous performance evaluations validate the system's robustness, showcasing exceptional accuracy and efficacy in tasks such as sentiment analysis and facial expression recognition. Comparative analyses highlight substantial enhancements in both student engagement and academic performance among participants utilizing the system compared to traditional instructional methods. Qualitative insights from students and educators further affirm the system's role in fostering motivation and improving instructional efficacy. This research underscores the transformative potential of integrating affective computing technologies into educational frameworks, paving the way for adaptive and emotionally attuned learning environments that cater to diverse learner needs effectively.