A Neural Architecture for Detecting Confusion in Eye-tracking Data

• URL: http://arxiv.org/abs/2003.06434v1
• Date: Fri, 13 Mar 2020 18:20:39 GMT
• Title: A Neural Architecture for Detecting Confusion in Eye-tracking Data
• Authors: Shane Sims and Cristina Conati
• Abstract summary: 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. Our model outperforms an existing model based on Random Forests resulting in a 22% improvement in combined sensitivity & specificity.
• Score: 1.8655840060559168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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