Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

The role of education in shaping future generations is crucial. The rapid advancement in Artificial Intelligence (AI) has prompted a considerable turn of events in the educational field. Advanced AI technologies have the potential to replace traditional classroom practices, giving more importance to students' preferences. Assessing the attention in students is the building block to improve both instructing and learning. Most of the time, it becomes puzzling for an instructor to track every student and know their interest in learning, especially in a vast scenario. In such cases, instructors are often unaware of the most coherent approach while teaching students with different learning capabilities. Therefore, this paper proposed a system that incorporates AI to develop a real-time vision-based smart classroom that autonomously monitors students' attention, as well as, emotions and gives live graphical feedback to the instructor. Our system was able to monitor the presence of a student in the classroom and analyze the students' attention and emotion. The proposed approach was able to assign numerical scores to the student for the concentration level in the class. The stated system can help the instructors evaluate the student's attention instantaneously and more accurately, thus supporting them in conducting the session suitably that may lead to better academic performance.