STUDENT LIVE BEHAVIOUR MONITORING IN ONLINE CLASS USING ARTIFICIAL INTELLIGENCE

Abstract

Due to the health emergency situation, which forced universities to stop using their centers as a
means of teaching, many of them opted for virtual education. Affecting the learning process of students, which
has predisposed many of them to become familiar with this new learning process, making the use of virtual
platforms more common. Many educational centers have come to rely on digital tools such as: Discord, Google
Meet, Microsoft Team, Skype and Zoom. The objective of the research is to report on the impact of student
learning through the use of the aforementioned videoconferencing tools. Surveys were conducted with teachers
and students who stated that 66% were not affected in their educational development. Monitoring students using
OpenCV shape landmarking models could involve tracking their facial landmarks in real-time to analyze their
attention levels, engagement, or emotional state during lectures or classes. OpenCV provides various tools and
libraries for face detection and facial landmark recognition. By utilizing these technologies, you can develop
applications to monitor students' facial expressions and gestures, providing insights into their interactions and
attentiveness during learning sessions