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."
