  COVID-19 has driven most schools to remote learning through online meeting
software such as Zoom and Google Meet. Although this trend helps students
continue learning without in-person classes, it removes a vital tool that
teachers use to teach effectively: visual cues. By not being able to see a
student's face clearly, the teacher may not notice when the student needs
assistance, or when the student is not paying attention. In order to help
remedy the teachers of this challenge, this project proposes a machine learning
based approach that provides real-time student mental state monitoring and
classifications for the teachers to better conduct remote teaching. Using
publicly available electroencephalogram (EEG) data collections, this research
explored four different classification techniques: the classic deep neural
network, the traditionally popular support vector machine, the latest
convolutional neural network, and the XGBoost model, which has gained
popularity recently. This study defined three mental classes: an engaged
learning mode, a confused learning mode, and a relaxed mode. The experimental
results from this project showed that these selected classifiers have varying
potentials in classifying EEG signals for mental states. While some of the
selected classifiers only yield around 50% accuracy with some delay, the best
ones can achieve 80% accurate classification in real-time. This could be very
beneficial for teachers in need of help making remote teaching adjustments, and
for many other potential applications where in-person interactions are not
possible.
