Abstract

In online learning environments, the need to monitor student engagement to achieve desired learning outcomes is foundational to any pedagogy for effective online teaching. With the absence of in-person supervision, keeping track of student attention in remote classes is quite arduous, if not impossible. This paper proposes an IoT-based EEG device for the real-time assessment of students' brain activity to measure and possibly improve students' attention levels in virtual classes. An ensemble machine learning (ML) study, specifically an artificial neural network (ANN) approach, is employed to investigate the relationship between student performance and EEG data. The study shows the performance of students is negatively correlated to the delta power and the theta/alpha ratio, the common EEG metrics for mental fatigue and drowsiness, respectively, for the student. An IoT-enabled EEG device provides teachers with real-time, precise, and unbiased data regarding the student's cognitive attention levels, via a reporting system. Thus, this research demonstrates that the proposed system, utilizing EEG and ANN ensemble-ML methods, can predict attention levels in real-time, enabling timely intervention for students who are disconnected. The study opens the door to the use of advanced BCI systems in teaching to maximize student attention.