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

A Neural Architecture for Detecting Confusion in Eye-tracking Data