Behavioural Indicators of Usability in Visual Analytics Dashboards

Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural indicators of usability when users conduct typical information-seeking and comparison tasks. In a first study (N=50), we identified strategies derived from 486,435 interaction events logged in a controlled setting with synthetic dashboards. User models consisting of these user strategies and graph literacy produced strong signals indicating that usability was predictable. In a second study (N=65), we tested the initial insights on real-world dashboards. While most of our hypotheses were confirmed, graph literacy emerged as the best predictor of usability. Usability was better predicted in dashboards with problems, suggesting promising opportunities for automated usability evaluation and real-time support for users struggling with visual analytics dashboards.

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Publication Details

Journal
ACM Transactions on Interactive Intelligent Systems
Published
2025-02-04
DOI
https://doi.org/10.1145/3715710
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
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article

Behavioural Indicators of Usability in Visual Analytics Dashboards

Sarah Clinch, Markel Vigo, Mohammed Alhamadi, Hatim Alsayahani
ACM Transactions on Interactive Intelligent Systems
Data Visualization and Analytics
article

Behavioural Indicators of Usability in Visual Analytics Dashboards

Sarah Clinch, Markel Vigo, Mohammed Alhamadi, Hatim Alsayahani
article en

Abstract

Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural indicators of usability when users conduct typical information-seeking and comparison tasks. In a first study (N=50), we identified strategies derived from 486,435 interaction events logged in a controlled setting with synthetic dashboards. User models consisting of these user strategies and graph literacy produced strong signals indicating that usability was predictable. In a second study (N=65), we tested the initial insights on real-world dashboards. While most of our hypotheses were confirmed, graph literacy emerged as the best predictor of usability. Usability was better predicted in dashboards with problems, suggesting promising opportunities for automated usability evaluation and real-time support for users struggling with visual analytics dashboards.

ACM Transactions on Interactive Intelligent Systems
King Abdulaziz City for Science and Technology (SA), University of Manchester (GB)
Openalex Percentile: Top 99%
Data Visualization and Analytics
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