Conducting eye-tracking studies on large and interactive process models using EyeMind

The understandability of process models has been subject to extensive research in which eye-tracking has demonstrated great capability to deliver meaningful insights. However, the full potential of this technology is not fully exploited due to the complexity of using dynamic stimuli in experiments (...

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Bibliographic Details
Published in:SoftwareX 2023-12, Vol.24, p.101564, Article 101564
Main Authors: Abbad-Andaloussi, Amine, Lübke, Daniel, Weber, Barbara
Format: Article
Language:English
Subjects:
BPMN
Dynamic areas of interest
Eye-tracking
Process models
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Summary:The understandability of process models has been subject to extensive research in which eye-tracking has demonstrated great capability to deliver meaningful insights. However, the full potential of this technology is not fully exploited due to the complexity of using dynamic stimuli in experiments (i.e., large and interactive process models) and the common use of static stimuli (i.e., small non-interactive models) as a cheap alternative limiting the ecological validity of the used experimental setting and the generalizability of the results. This paper presents EyeMind, a solution to overcome this limitation by supporting the whole experimental workflow using dynamic stimuli and offering a comprehensive analysis toolkit of eye-tracking data. All these features facilitate experiments on large and interactive process models as well as the extraction of meaningful insights.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2023.101564