Student Expression Detection in E-Learning Platforms

Abstract

Our research suggests a deep learning method for determining how engaged students are with their online courses. Our method makes use of convolutional neural networks (CNNs) to interpret facial expressions that are recorded via webcams during online conversations. To provide the best possible model input, we preprocess face photos and select a varied dataset. The CNN architecture captures both spatial and temporal dependencies, enhancing engagement level detection accuracy. Our model performs well in classifying expressions that indicate curious, distracted, enthusiastic, observant, uninterested through training and validation. In conclusion, that the online learning experience may be enhanced by using deep learning algorithms for engagement detection, making it more advantageous and successful for students. KEYWORDS – CNN, Webcams, Engagement level, Online learning, Deep Learning, Facial expressions