Students Live Behavior Monitoring In Online Classes

Abstract

Nowadays, we all know that due to the health emergency situation most of the universities and colleges opted for the virtual education. In this pandemic the student's learning process was affected mostly. So due to that purpose most of the educational centers proposed the virtual system. Many educational centers started their classes on different virtual tools such as Discord, Google Meet, Microsoft Teams, Skype and Zoom. So, the main objective of this paper is on the impact of student learning in video conferences. Most of the teachers were familiar with those tools; However, about 24% of them stated that their academic performance has been improved. But some of the teachers were facing some of the difficulties on a psychological level due to the new teaching procedure. In this paper we are using Artificial intelligence and Machine Learning algorithms to analyze the student’s behavior. After this analysis to prove that students and teachers agree that these virtual tools are very helpful in the online classes. "The challenges of this paper are that the teacher may know the student’s learning activity rate in online classes".The primary of this project is to develop a self-contained agent that can provide information to both teachers and students. Important academic outcomes such as critical thinking and the grades students receive in a topic are closely tied to the extent of student involvement.