Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks
This work addresses confusion detection for users of interactive visualization tools, but it is incremental as it applies an existing deep learning method to a new domain.
The paper tackled the problem of detecting confusion from eye-tracking data using Recurrent Neural Networks (RNNs), achieving a sensitivity of 0.74 and specificity of 0.71, which outperformed a Random Forest classifier with 0.51 sensitivity and 0.70 specificity.
Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization tool), we found that RNNs learn a feature representation from the raw data that allows for a more powerful classifier than previous methods that use engineered features. This is evidenced by the stronger performance of the RNN (0.74/0.71 sensitivity/specificity), as compared to a Random Forest classifier (0.51/0.70 sensitivity/specificity), when both are trained on an un-augmented dataset. However, using engineered features allows for simple data augmentation methods to be used. These same methods are not as effective at augmentation for the feature representation learned from the raw data, likely due to an inability to match the temporal dynamics of the data.