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Analysis of Students’ Concentration Levels for Online Learning Using Webcam Feeds

Tracking the concentration of students during online learning offers great benefits. For examples, distracted students can be suggested to do a brief exercise to refresh their brains; or the teacher can be notified when too many students have difficulties on concentration so the class could take a short break. Traditionally, mental states like concentration levels can be analyzed using Electroencephalogram (EEG) or Functional Near-Infrared Spectroscopy (fNIRS). However, methods that utilize these data require specialized equipment which is not feasible to deploy on a large scale. On the other hand, recent breakthroughs in deep learning provide possibilities of scalable solutions to detect concentration levels using only webcam. Leveraging this advancement, we investigate the task of tracking students’ concentration levels during online learning using facial data coupled with deep learning based computer vision technologies. More specifically, we examine the performances of different representations of facial data integrated with various deep architectures to empirically determine a solution balanced between prediction accuracy and time efficiency that is suitable for real-time application. Our experimental study shows that the proposed solution achieves over 91% accuracy while keeping execution time low enough for real-time deployment.