ETGraph: A graph-based approach for visual analytics of eye-tracking data

Abstract

Mind wander(ing) (MW) or zoning out is a ubiquitous phenomenon where attention involuntary shifts from task-related processing to task-unrelated thoughts. Unfortunately, MW is a highly internal state so it cannot be readily inferred from overt behaviors and expressions. To help experts investigate mind wanderings, we present a graph-based approach for visual analytics of eye-tracking data, which utilizes the graph representations to illustrate the reading patterns and further help experts detect and verify mind wanderings based on the graph structures and other graph attributes. The input data are collected from multiple participants reading multiple pages of a book on a computer screen. Our approach first clusters fixations into fixation clusters, then creates the eye-tracking graph, i.e., ETGraph, for use in conjunction with the standard page view, time view, and statistics view. The graph view presents a visual representation of the actual reading patterns of a single participant or multiple participants and therefore serves as the main visual interface for exploration and navigation. We design a suite of techniques to help users identify common reading patterns and outliers for analytical reasoning at three different levels of detail: single participant single page, single participant multiple pages, and multiple participants single page. Interactive querying and filtering functions are provided for reducing visual clutter in the visualization and enabling users to answer questions and glean insights. Our tool also facilitates the detection and verification of mind wandering that the experts seek to investigate. We conduct a user study and an expert evaluation to assess the effectiveness of ETGraph in terms of its visual summarization and comparison capabilities.