  In response to the COVID-19 pandemic, traditional physical classrooms have
transitioned to online environments, necessitating effective strategies to
ensure sustained student engagement. A significant challenge in online teaching
is the absence of real-time feedback from teachers on students learning
progress. This paper introduces a novel approach employing deep learning
techniques based on facial expressions to assess students engagement levels
during online learning sessions. Human emotions cannot be adequately conveyed
by a student using only the basic emotions, including anger, disgust, fear,
joy, sadness, surprise, and neutrality. To address this challenge, proposed a
generation of four complex emotions such as confusion, satisfaction,
disappointment, and frustration by combining the basic emotions. These complex
emotions are often experienced simultaneously by students during the learning
session. To depict these emotions dynamically,utilized a continuous stream of
image frames instead of discrete images. The proposed work utilized a
Convolutional Neural Network (CNN) model to categorize the fundamental
emotional states of learners accurately. The proposed CNN model demonstrates
strong performance, achieving a 95% accuracy in precise categorization of
learner emotions.
