Attention Recognition System in Online Learning Platform Using EEG Signals

The current scenario of education has been transformed from paper to paperless world. With the advancement in technology, smart education concept, and internet access, an adaptive learning environment has been created due to which participation of online learners across the smaller cities has been increased. In the online learning platform, there is a need to identify students' mental state so as to motivate them and to help them deepen their learning performance. The current stage of the online education system does not focus on providing immediate feedback and thus results in a lower performance of the user. The immediate feedback from student-to-teacher and teacher-to-student is not possible in online learning environments, unlike traditional classroom environments. To help online course teachers to effectively observe students' state of attention in online learning environments. This paper proposes a solution based on the EEG feedback approach. While watching online course video, a single electrode EEG device is worn by the user will keep on analyzing the brain activity to measure the attention level of the student. The interesting results for student learning assessments produced by the analysis of the raw data from the EEG device. This will ensure that the online user is able to receive constant feedback in real time while watching online course videos, thus improving the user's overall learning experience.