A Neural Architecture for Detecting User Confusion in Eye-tracking Data

Abstract

Encouraged by the success of deep learning in a variety of domains, we investigate the effectiveness of a novel application of such methods for detecting user confusion with 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 a Random Forest classifier, resulting in a 22% improvement in combined confused & not confused class accuracies.