Key result
A Convolutional Neural Network achieved 79.2% accuracy in classifying student mental states from EEG signals, while XGBoost achieved 74.9% accuracy with significantly faster processing time.
Absolute Event Rate: 79.2% vs 33%
Machine learning models applied to EEG data can classify student mental states during remote learning with up to 80% real-time accuracy.
Supports AI tools to detect student needs in remote classes; leaves open effects on learning outcomes.
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.
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Daben Kyle Liu (2021) studied Mental stress and cognitive states during remote learning. Convolutional Neural Network (CNN) and XGBoost classifiers vs. Support Vector Machine (SVM), Deep Neural Network (DNN), and random classification was evaluated on Classification accuracy of mental states (engaged, confused, relaxed). A Convolutional Neural Network achieved 79.2% accuracy in classifying student mental states from EEG signals, while XGBoost achieved 74.9% accuracy with significantly faster processing time.
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