Classification of Students’ Attentional States Using Attention Mechanism and BiLSTM Fusion

Abstract

At present, most deep learning-based analysis of student’s attentional states in class has been studied only for a single model structure, and there is not enough recognition accuracy. To address this issue, an attention classification model FF-BiALSTM is proposed, which integrates an Attention Mechanism and a bi-directional long short-term memory neural network (Bi-LSTM). The Attention Mechanism is used to capture global features better and two Bi-LSTM layers are employed to capture time-domain features more effectively. This study defined two attention states to identify whether students are focused or not. Experiments on the Student EEG and Student Reading datasets show that this algorithm can effectively improve student attention classification performance. This experiment obtained 97.77% accuracy on the Student EEG training set and 91.35% on the Student EEG testing set.