Neural Network-Based Affective Computing for Education

Abstract

In recent years, the intersection of technology and education has opened up exciting new avenues for enhancing the learning experience. One of the most promising developments in this realm is affective computing, which focuses on the ability of systems to recognize and respond to human emotions. This paper delves into the application of neural networks in affective computing, specifically targeting the detection and interpretation of students' emotions in educational settings. By leveraging advanced machine learning techniques, I aimed to create a more responsive and engaging learning environment that adapts to the emotional states of students. Through a comprehensive literature review, I identified existing gaps in research, particularly the need for integrated approaches that consider multiple modalities of emotion recognition. My methodology involves collecting a diverse dataset from students, encompassing facial expressions, voice recordings, and physiological signals. I then develop a multi-modal neural network model that combines these inputs to accurately classify emotions. The results of my study reveal that implementing this technology can significantly enhance student engagement and motivation, leading to improved academic performance. By providing real-time feedback to educators, I empower them to tailor their teaching strategies to better support their students' emotional needs. Ultimately, this research highlights the transformative potential of neural network-based affective computing in education, paving the way for more personalized and effective learning experiences.

Keywords: Affective Computing, Neural Networks, Education, Emotion Detection, Student Engagement, Machine Learning, Deep Learning, Emotion Recognition