Student Behavior Analysis using Deep Learning

EasyChair Preprint 15994

Abstract

This research investigates the use of advanced learning algorithms to monitor significant classroom student activities, including Sleeping, Engaging, and Cheating. It employs convolutional neural networks for identifying patterns and artificial neural networks for classifying these student activities in real-time. The framework provides educators with actionable insights, enhancing classroom participation strategies and classroom management. Practical challenges such as data privacy and computational demands are discussed alongside future research opportunities for expansion potential implementation

Keyphrases: Artificial Neural Networks, Convolutional Neural Networks, Modern machine learning techniques, Scalability, Student student activity, Video Analytics, classroom, computational, data privacy, educational technology, engagement detection, environment, real-time analysis, resources

@booklet{EasyChair:15994,
  author    = {Chethan P J and Naveen M.S and Syed Waseem I and Uzair Ahamad},
  title     = {Student Behavior Analysis using Deep Learning},
  howpublished = {EasyChair Preprint 15994},
  year      = {EasyChair, 2025}}