Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 101564 |
| Seitenumfang | 7 |
| Fachzeitschrift | SoftwareX |
| Jahrgang | 24 |
| Frühes Online-Datum | 31 Okt. 2023 |
| Publikationsstatus | Veröffentlicht - Dez. 2023 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Angewandte Informatik
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in: SoftwareX, Jahrgang 24, 101564, 12.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Conducting eye-tracking studies on large and interactive process models using EyeMind
AU - Abbad-Andaloussi, Amine
AU - Lübke, Daniel
AU - Weber, Barbara
N1 - Funding Information: This work is supported by the International Postdoctoral Fellowship (IPF) from the University of St. Gallen, Switzerland (Number: 1031574 ). The funding organizations had no involvement in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to publish the article.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - BPMN
KW - Dynamic areas of interest
KW - Eye-tracking
KW - Process models
UR - http://www.scopus.com/inward/record.url?scp=85175076732&partnerID=8YFLogxK
U2 - 10.1016/j.softx.2023.101564
DO - 10.1016/j.softx.2023.101564
M3 - Article
AN - SCOPUS:85175076732
VL - 24
JO - SoftwareX
JF - SoftwareX
SN - 2352-7110
M1 - 101564
ER -