LAUSR

Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To overcome this issue, we implemented an online learning system based on electroencephalography (EEG)-based passive brain-computer interface technology referred to as the “adaptive neuro-learning system (ANLS).” It monitors the current mental states of learners seamlessly using EEG signals. Then, it adaptively provides natural and interactive video feedback rather than simple alarms or pop quizzes following the current mental conditions of a learner. In this article, a total of 60 university students were assigned randomly to one of four groups: two experimental groups, for which either ANLS based on attention state estimation or ANLS based on both attention and comprehension states estimation was tested, and two control groups, the students in which were taught using either the conventional online lecture without feedback or an online course with randomized video feedbacks. Each member of these groups attended a 53 min open courseware video lecture. Then, the educational effects of the proposed system were evaluated quantitatively via a written examination. Our results revealed a significantly higher learning performance for the experimental group (average test score of the experimental groups = 83.83 and that of the control groups = 56.67), demonstrating the feasibility of the proposed education strategy.