An adaptive system for predicting student attentiveness in online classrooms

Abstract

The effectiveness of teaching methods is determined by whether a student is attentive in a lecture or not. In face-to-face classroom teaching, a teacher is able to judge whether students are understanding the subject, based on their facial expressions. However, since the uprise of COVID-19 pandemic, virtual classrooms have found a holding in the field of education and detecting attentiveness of students is a challenge in the same. This paper proposes a student attentiveness model that would detect and monitor a student’s eye state to determine their level of attentiveness and provide a real-time feedback mechanism to the teacher. The proposed model employs a histogram of oriented gradient (HOG) method in conjunction with support vector machine (SVM) algorithm for face recognition. It then computes an adaptive eye aspect ratio (AEAR) for each individual student to determine their level of attentiveness. The model is tested on a real-time dataset and validated using classifiers (SVM, decision tree, and random forest). The results of the classifiers verify that the model produces an accuracy of more than 92%.