Characteristic Behaviors of Elementary Students in a Low Attention State During Online Learning Identified Using Electroencephalography

With the widespread application of online education platforms, the necessity for identifying learner's mental states from webcam videos is increasing as it can be potentially applied to artificial intelligence-based automatic identification of learner's states. However, the behaviors that elementary school students frequently exhibit during online learning particularly when they are in a low attention state have rarely been investigated. This study employed electroencephalography (EEG) to continuously track changes in the learner's attention state during online learning. A new EEG index reflecting elementary students' attention level was developed using an EEG dataset acquired from 30 fourth graders during a computerized d2 test of attention. Characteristic behaviors of 24 elementary students in a low attention state were then identified from the webcam videos showing their upper bodies captured during 40-minute online lectures, with the proposed EEG index being used as a reference to determine their attention level at the time. Various characteristic behaviors were identified regarding participant's mouth, head, arms, and torso. For example, opening mouth or leaning back was observed more frequently in a low attention state than in a high attention state. It is expected that the characteristic behaviors reflecting learner's low attention state would be utilized as a useful reference in developing more interactive and effective online education systems.