A Deep Learning Based Student Engagement Monitoring System for Online Learning

Author(s)	Mr. Kailash Chandra, Ms. Gerada Jayasri, Mr. Jakkula Sampreeth, Mr. Bathula Mahesh Babu, Ms. Chokkakula Devi
Country	India
Abstract	Facial Emotion Recognition (FER) is an important research topic in artificial intelligence and computer vision fields, allowing for the analysis of emotions through facial expressions. As online learning platforms are increasing in number and usage, FER is an important aspect of student engagement analysis. In this paper, a deep learning framework is suggested to address FER using face detection and classification using a fine-tuned DenseNet 121 model. The system captures faces from online sessions and uses FER to analyze emotions and generate a dashboard to display student engagement analytics in real time. The experimental results show that the suggested framework provides a good balance between precision and computational efficiency compared to other models such as VGG16 and ResNet50. The suggested framework is important for increasing awareness about student engagement in online learning sessions
Keywords	Facial Emotion Recognition, Student Engagement, Deep Learning, Convolutional Neural Network, OnlineLearning, Educational Analytics,DenseNet-121,Engagement Analysis.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-03

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