Conducting eye-tracking studies on large and interactive process models using EyeMind
Journal
SoftwareX
ISSN
2352-7110
Type
journal article
Date Issued
2023
Author(s)
Abstract
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.
Volume
24