Artificial Intelligence for Sustainable Applications

12Virtual Teaching Activity Monitor

Sakthivel S.* and Akash Ram R.K.

Department of Electronics and Communications Engineering, Amrita Vishwa, Vidyapeetham, Chennai, India

Abstract

In the current COVID-19 scenario, every aspect of daily life is reliant on the virtual world, and a virtual classroom is no exception. During this pandemic, we have learned that adjusting to a new normal is the best way to survive. As a result, online classes became the sole way to get an education. Adapting teaching tactics to the students’ attention status can make eLearning platforms more personalized. Students’ attention states are divided into three categories: attentive, drowsiness, and yawning. The analysis of student involvement helps to nurture those emotions and behavioral patterns that are helpful to gaining knowledge, therefore boosting the instructional process effectively. To keep an eLearning classroom running well, you’ll need the correct monitoring software. The suggested attention estimation technique aims to bridge the gap between an online classroom and a traditional offline classroom. So, I felt these add-ons are required: Network Speed, Attention Detector, Head Movements, Efficient Attendance system, and Text classification. By implementing these add-ons, we can achieve student-centered teaching.

Keywords: eLearning, attention states, head pose estimation, attendance’s system, Eye Aspect Ratio (EAR), facial feature detection

12.1 Introduction

A teacher can instantly detect ...