Analysis of Students’ Emotions in Real-Time During Class Sessions Through an Emotion Recognition System

Recognizing and responding to students’ emotional states during classes is crucial for optimizing learning outcomes, yet it remains challenging for educators. This study presents the development and implementation of a real-time emotion recognition system using convolutional neural networks (CNNs) to analyze students’ facial expressions during in-person classes. Trained on the FER2013 dataset, the system classifies seven distinct emotions with 85% accuracy. An experiment with 20 university students aged 18–25 compared emotional responses across three teaching methodologies: collective, group, and experiential. Experiential teaching elicited the most positive emotions, with 50% of expressions classified as happiness, while collective teaching generated more negative responses. Statistical analysis revealed a significant positive correlation between happiness and academic performance (r = 0.65, p < 0.01) and a negative correlation between fear and performance (r = −0.54, p < 0.05). The system provides educators with quantitative emotional data, enabling real-time adaptation of teaching strategies and retrospective analysis of class dynamics. This research contributes to AI-enhanced education, offering insights into creating more responsive and student-centered learning environments while addressing privacy and data protection considerations throughout the study.