GaRSIVis: Improving the Predicting of Self-Interruption during Reading using Gaze Data

Jan Pilzer, Shareen Mahmud, Vanessa Putnam, and Tamara Munzner. Proc. ETVIS 2018.





Gaze pattern data provides a promising opportunity to create a predictive model of self-interruption during reading that could support active interventions to keep a reader's attention at times when self-interruptions are predicted to occur. We present two systems designed to help analysts create and improve such a model. We present GaRSIVis, (Gaze Reading Self-Interruption Visualizer), that integrates a visualization front-end suitable for data cleansing and a prediction back-end that can be run repeatedly as the input data is iteratively improved. It allows analysts refining the predictive model to filter out unwanted parts of the gaze data that should not be used in the prediction. It relies on data gathered by GaRSILogger, which logs gaze data and activity associated with interruptions during on-screen reading. By integrating data cleansing and our prediction results in our visualization, we enable analysts using GaRSIVis to come up with a comprehensible way of understanding self-interruption from gaze related features.

High-Resolution Figures

Last modified: Jun 16 2018.