A Neural Architecture for Detecting Confusion in Eye-tracking Data

13 Mar 2020 Shane Sims Cristina Conati

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... (read more)

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