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A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics.
IEEE Transactions on Visualization and Computer Graphics
|October 30, 2023
Summary
This study introduces a practical methodology to evaluate guidance in visual analytics (VA). It validates the approach using expert and end-user studies, offering best practices for future VA guidance evaluations.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Data Visualization
Background:
- Guidance in visual analytics (VA) aids users in complex data exploration and analysis.
- Prior research has focused on theoretical aspects and implementation of guidance in VA.
- Evaluating the effectiveness of guidance-enhanced VA solutions remains a significant research gap.
Purpose of the Study:
- To introduce and validate a practical methodology for evaluating guidance in visual analytics.
- To identify key quality criteria for guidance in VA and gather expert validation.
- To develop heuristics for both expert and end-user evaluations of guidance-enhanced VA systems.
Main Methods:
- Developed a practical evaluation methodology for guidance in visual analytics.
- Identified eight quality criteria and collected expert feedback on their validity.
- Derived two sets of heuristics for expert evaluations and end-user studies.
- Applied the methodology to two case studies: a research prototype and a public recommender system.
Main Results:
- The proposed methodology was validated through practical application in two distinct studies.
- Expert feedback confirmed the validity of the identified quality criteria for guidance evaluation.
- The dual approach (expert and end-user studies) provided comprehensive insights into guidance quality.
- Identified practical challenges and best practices for conducting future evaluations.
Conclusions:
- A robust and practical methodology for evaluating guidance in visual analytics has been established and validated.
- The derived heuristics and quality criteria offer a structured approach for assessing guidance-enhanced VA systems.
- The findings provide valuable insights and recommendations for researchers and practitioners in the field of visual analytics.
- This work addresses the open research question of evaluating guidance in visual analytics, paving the way for more effective systems.
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