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In response to the COVID-19 pandemic, traditional physical classrooms havetransitioned to online environments, necessitating effective strategies toensure sustained student engagement. A significant challenge in online teachingis the absence of real-time feedback from teachers on students learningprogress. This paper introduces a novel approach employing deep learningtechniques based on facial expressions to assess students engagement levelsduring online learning sessions. Human emotions cannot be adequately conveyedby a student using only the basic emotions, including anger, disgust, fear,joy, sadness, surprise, and neutrality. To address this challenge, proposed ageneration of four complex emotions such as confusion, satisfaction,disappointment, and frustration by combining the basic emotions. These complexemotions are often experienced simultaneously by students during the learningsession. To depict these emotions dynamically,utilized a continuous stream ofimage frames instead of discrete images. The proposed work utilized aConvolutional Neural Network (CNN) model to categorize the fundamentalemotional states of learners accurately. The proposed CNN model demonstratesstrong performance, achieving a 95% accuracy in precise categorization oflearner emotions.

Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning