Classroom engagement evaluation using 360-degree view of the camera with deep learning techniques

Measuring classroom engagement is an important but challenging task in education. In this paper, we present an automated method for the assessment of the degree of classroom engagement using computer vision techniques that integrate data from multiple sensors, including the front and back of the student's seating arrangement. The students' engagement is evaluated based on attributes such as facial expression, gesture, head position, and distractions visible from the frontal view of the students. Moreover, using the videos from the back of the classroom, the professor's teaching content as well as their alignment with student engagement, are calculated. We leverage deep learning methods to extract emotion and behavior features to aid in the evaluation of engagement. These AI methods will quantify the classroom engagement process.