Analyzing students’ concentration in online courses through Webcam

Abstract

Online learning is growing in popularity these days. As a result, students typically contribute millions of course-related responses to discussion forums and exchange some learning experiences. This study focuses on online courses offered through MOOC platforms and identifies the variables that affect students’ ability to stay focused. We suggest a unique method to address this issue by evaluating students’ levels of concentration using the CNN architecture, MobileNetV2, VGG16, ResNet50, and InceptionV3 models. Our goal is to determine whether the issue is with students’ concentration, the course material, or both. Measurement of concentration levels, evaluation of video data, comparison of model performances, and provision of class-based concentration levels (attentive, inattentive, and sleepy) are the goals of our research. The dataset underwent pre-processing, which included resizing for analysis, frame extraction, and annotation for classification. Our research offers educators insightful information that will help them to increase the overall efficacy of online learning. Furthermore, the study advances the area by offering a methodical technique for assessing and evaluating students’ concentration on online courses.