Engagement Detection in Online Lectures

Abstract

" With the current state of the world, online learning has become the dominant means of acquiring information, and every university and school has begun to deliver lectures through an online platform. As a result, online learning has become the primary method of disseminating information in the world. A gap that the author identified is the absence of a proper tutoring system that can identify student interest while studying. In online learning, improving learners' interaction with their educational experiences is a major concern. As a result, the value of tutoring programs that can provide a more personalized and well focused learning experience becomes noticeable at this stage. As a result, the author's project methodology was extremely helpful in overcoming this issue, and he made suggestions for future improvements in interaction detection technology for online education. Since there were many methods for detecting engagement, including automatic, semiautomated, and manual, the author chose the automatic approach for implementing a framework to detect student engagement in online lectures. And the author decided to build a system that would detect facial recognition, especially eye gaze. The author uses image processing and machine learning to implement the framework in this approach. So in this paper Author will discuss about the technologies that he used, Approach of the Implementation and how he managed to implement the system widely."