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Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems

Authors:

Moritz Spiller,

Ying-Hsang Liu,

Md Zakir Hossain,

Julia Geissler,

Andreas NürnbergerAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 11, Issue 2

Article No.: 14, Pages 1 - 25

https://doi.org/10.1145/3446638

Published: 20 May 2021 Publication History

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Abstract

Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension, however, depends on individual factors such as their cognitive abilities. The research literature provides evidence that user-adaptive information visualizations positively impact the users’ performance in visualization tasks. This study attempts to contribute toward the development of a computational model to predict the users’ success in visual search tasks from eye gaze data and thereby drive such user-adaptive systems. State-of-the-art deep learning models for time series classification have been trained on sequential eye gaze data obtained from 40 study participants’ interaction with a circular and an organizational graph. The results suggest that such models yield higher accuracy than a baseline classifier and previously used models for this purpose. In particular, a Multivariate Long Short Term Memory Fully Convolutional Network shows encouraging performance for its use in online user-adaptive systems. Given this finding, such a computational model can infer the users’ need for support during interaction with a graph and trigger appropriate interventions in user-adaptive information visualization systems. This facilitates the design of such systems since further interaction data like mouse clicks is not required.

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Cited By

Fu B(2024)Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class HierarchiesMultimodal Technologies and Interaction10.3390/mti81000908:10(90)Online publication date: 12-Oct-2024
https://doi.org/10.3390/mti8100090
Jeong DJeong BJi S(2024)Leveraging Machine Learning to Analyze Semantic User Interactions in Visual AnalyticsInformation10.3390/info1506035115:6(351)Online publication date: 13-Jun-2024
https://doi.org/10.3390/info15060351
Yanez FConati COttley ANobre C(2024)The State of the Art in User‐Adaptive VisualizationsComputer Graphics Forum10.1111/cgf.15271Online publication date: 4-Dec-2024
https://doi.org/10.1111/cgf.15271
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Index Terms

Predicting Visual Search Task Success from Eye Gaze Data as a Basis for User-Adaptive Information Visualization Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Visualization
    1. Visualization application domains
      1. Information visualization

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cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 11, Issue 2

June 2021

267 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3465444

Editor:
Michelle X. Zhou
Juji, Inc., USA

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Publication History

Published: 20 May 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 June 2020

Published in TIIS Volume 11, Issue 2

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https://doi.org/10.3390/mti8100090
Jeong DJeong BJi S(2024)Leveraging Machine Learning to Analyze Semantic User Interactions in Visual AnalyticsInformation10.3390/info1506035115:6(351)Online publication date: 13-Jun-2024
https://doi.org/10.3390/info15060351
Yanez FConati COttley ANobre C(2024)The State of the Art in User‐Adaptive VisualizationsComputer Graphics Forum10.1111/cgf.15271Online publication date: 4-Dec-2024
https://doi.org/10.1111/cgf.15271
Chen S(2024)The application of eye-tracking technology in chemistry education research: a systematic reviewResearch in Science & Technological Education10.1080/02635143.2024.2435343(1-20)Online publication date: 4-Dec-2024
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Steichen B(2024)Computational Methods to Infer Human Factors for Adaptation and Personalization Using Eye TrackingA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_7(183-204)Online publication date: 1-May-2024
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Sherbina D(2022)Chronometry of distractor views to discover the thinking process of students during a computer knowledge testBehavior Research Methods10.3758/s13428-021-01743-x54:5(2463-2478)Online publication date: 7-Feb-2022
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