  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.
