Abstract: The rapid transformation of education into digital platforms has emphasized the need to improve virtual learning experiences by understanding students' emotions during lectures. Emotional states directly impact students’ focus, engagement, and learning outcomes, making real-time emotion analysis a valuable tool for enhancing teaching methodologies. This research presents an advanced emotion-based interactive dashboard designed to analyse students' facial expressions during online lectures, offering actionable insights to educators for improving teaching strategies and engagement. The system uses Convolutional Neural Networks (CNNs) for facial expression analysis and classifies emotions into categories such as happiness, sadness, anger, surprise, fear, and neutrality. These models are trained using both the FER-2013 and CK+ datasets, which provide robustness across varied image quality and expression types. To optimize training performance and improve model convergence, the system employs the Adam Optimizer, an adaptive learning rate optimization algorithm that combines the benefits of both AdaGrad and RMSProp, ensuring faster and more reliable training of deep neural networks. The processed emotional data is integrated into an intuitive dashboard that combines contextual details, such as the subject being taught, teaching faculty, and session-specific parameters. The dashboard offers dynamic visualization of emotion distribution, engagement trends, and real-time analytics, enabling educators to identify patterns in student behaviour. The system demonstrated high accuracy in emotion classification under various conditions. The integration of emotion-based analytics provides a unique approach to monitoring class engagement, identifying struggling students, and fostering personalized learning experiences. By combining advanced deep learning techniques with real-time analytics, the proposed system has the potential to redefine the future of online education, making it more responsive, adaptive, and student-centered
Keywords: Convolutional Neural Network, Analytical Dashboard, Adam Optimizer, Emotion Classification
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