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Related Experiment Video

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Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

A systematic review on the practice of evaluating visualization.

Tobias Isenberg¹, Petra Isenberg, Jian Chen

¹INRIA.

IEEE Transactions on Visualization and Computer Graphics

|September 21, 2013

Summary

This study systematically reviewed evaluation practices in IEEE Visualization papers. While image and algorithm evaluations remain dominant, participant-based evaluations, including work practices and reasoning, have steadily increased over time.

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Last Updated: May 7, 2026

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

Area of Science:

Computer Science
Human-Computer Interaction
Data Visualization

Background:

Evaluation practices in visualization research are crucial for assessing tool effectiveness.
Understanding the evolution of these practices provides insights into community priorities.
Previous work by Lam et al. [2012] established a foundation for evaluating visualization research.

Purpose of the Study:

To systematically review and analyze evaluation practices in papers from the IEEE Visualization conference over ten years.
To understand the characteristics, goals, and evolution of evaluation methods used in the field.
To compare evaluation trends between the IEEE Visualization and IEEE Information Visualization conferences.

Main Methods:

Conducted a systematic literature review of ten years of papers from the IEEE Visualization conference.
Utilized and extended an existing coding scheme (Lam et al. [2012]) to categorize evaluation types and goals.
Analyzed the frequency and evolution of different evaluation methods over time.

Main Results:

Evaluations of resulting images and algorithm performance are consistently the most prevalent (80-90% of papers since 1997).
A steady increase in participant-based evaluations (performance, subjective feedback, work practices, analysis/reasoning) observed in recent years.
IEEE Visualization showed a more pronounced trend towards participant-based evaluations than IEEE Information Visualization until 2010, after which both conferences showed similar increases.

Conclusions:

The field shows a growing interest in user-centered evaluation methods beyond just visual output and algorithm efficiency.
Informal reporting of requirements analyses and domain-specific work practices hinders cross-comparison and external validity.
Further standardization in reporting user-centric evaluation details is needed to enhance the rigor and comparability of visualization research.