Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks

19 Jun 2019Shane D. SimsVanessa PutnamCristina Conati

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization tool), we found that RNNs learn a feature representation from the raw data that allows for a more powerful classifier than previous methods that use engineered features... (read more)

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