  Encouraged by the success of deep learning in a variety of domains, we
investigate a novel application of its methods on the effectiveness of
detecting user confusion in eye-tracking data. We introduce an architecture
that uses RNN and CNN sub-models in parallel to take advantage of the temporal
and visuospatial aspects of our data. Experiments with a dataset of user
interactions with the ValueChart visualization tool show that our model
outperforms an existing model based on Random Forests resulting in a 22%
improvement in combined sensitivity & specificity.
