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.

翻译：为应对COVID-19疫情，传统线下课堂已转向线上环境，亟需有效策略来保障学生持续的学习参与度。在线教学的一个重大挑战是教师无法实时获取学生学习进程的反馈。本文提出了一种基于深度学习的创新方法，通过分析面部表情来评估学生在在线学习过程中的参与度水平。仅凭愤怒、厌恶、恐惧、喜悦、悲伤、惊讶和中性这七种基本情绪，学生难以充分表达人类情感。为解决该问题，本研究通过组合基本情绪生成了困惑、满意、失望、沮丧四种复杂情绪——学生在学习过程中往往同时经历这些复杂情绪。为动态呈现这些情绪，本文采用连续图像帧序列而非离散图像。研究采用卷积神经网络（CNN）模型对学习者的基本情绪状态进行精确分类。实验表明，所提出的CNN模型表现优异，在学习者情绪分类准确率上达到95%。