Key result
LSTM with attention improves EEG-based student confusion detection to ~82% accuracy versus basic LSTM.
Why the study?
Confusion among students hinders learning and contributes to demotivation, but identifying confused pupils in extensive courses requires significant time and resources.
Absolute Event Rate: 0.817% vs 0.773%
A deep learning model using LSTM and Attention can detect student confusion from EEG signals with 0.82 accuracy.
May support EEG-based student engagement tools; leaves open prospective validation before educational adoption.
Confusion among students hinders learning and contributes to demotivation and disinterest in the course materials. However, it takes a lot of time and resources to identify confused pupils in extensive courses. Using LSTM and Attention, we suggest a deep learning model for monitoring students' confusion by EEG signals from students when they watching MOOC videos. The model obtained an accuracy of 0.82 on the EEG data, exceeding the previous experimental results for this dataset. Experiments show that the attention mechanism picks up on the significance of various features on prediction results. It can effectively solve the overfitting problem and improve the model classification effect.
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Xia Li (2022) studied Confusion during online learning (n=10). LSTM with Attention model vs. Basic LSTM model was evaluated on Classification accuracy. An LSTM model with an attention mechanism achieved 81.7% accuracy in detecting student confusion from EEG signals, improving upon the 77.3% accuracy of the basic LSTM model.
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