Concentration Estimation in Online Video Lecture Using Multimodal Sensors

Distance learning is one of the technology-wise challenges in the education field. Remote learning provides the advantage of encouraging anyone to join from worldwide. In order to make education sustainable, understanding students' concentration in remote study is significant. In this study, we evaluate multi-modal sensors for estimating students' concentration levels during online classes. We collect sensor data such as accelerometers, gyroscopes, heart rates, facial orientations, and eye gazes. We conducted experiments with 13 university students in Japan. The results of our study, with an average accuracy rate of 74.4% for user-dependent cross-validation and 66.3% for user-independent cross-validation, have significant implications for understanding and improving student engagement in online learning environments. Most interestingly, we found that facial orientations are significant for user-dependent and eye gazes for user-independent classification.