Published Paper Details:

In the realm of virtual education, understanding and enhancing student engagement is paramount for effective teaching and learning experiences. This study delves into the application of deep learning methodologies, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms, to analyse facial expressions as indicators of student involvement during virtual classes. Through the utilization of webcams and image processing techniques, facial expressions captured in real-time are fed into RNN and LSTM models trained to recognize patterns indicative of varying degrees of engagement. The study aims to establish a robust framework for quantifying engagement levels based on facial expressions, enabling educators to gain insights into student attentiveness, interest, and comprehension during virtual classes. By leveraging deep learning techniques, which excel in handling complex and dynamic data, this research seeks to overcome the limitations of traditional methods and provide more accurate and actionable assessments of student engagement. this research represents a significant advancement in the use of technology for assessing student engagement in virtual education settings. By integrating deep learning algorithms with facial expression analysis, this study opens new avenues for enhancing the effectiveness and impact of online teaching and learning experiences.