Gaze-based attention network analysis in a virtual reality classroom
(2024) In MethodsX 12.- Abstract
This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and... (More)
This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and interconnectedness of the networks were calculated to describe the network structure. The measures, derived from graph theory, allowed for statistical inference testing and the interpretation of participants' visual attention in 3D VR environments. Our method provides useful insights when analyzing students' learning in a VR classroom, as reported in a corresponding evaluation article with N = 274 participants.
(Less)
•Guidelines on implementing gaze-ray casting in VR using the Unreal Engine and the HTC VIVE Pro Eye.
•Creating gaze-based attention networks and analyzing their network structure.
•Implementation tutorials and the Open Source software code are provided via OSF: https://osf.io/pxjrc/?view_only=1b6da45eb93e4f9eb7a138697b941198.
- author
- Stark, Philipp LU ; Hasenbein, Lisa ; Kasneci, Enkelejda and Göllner, Richard
- publishing date
- 2024-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Network analysis, Eye tracking, Virtual Reality, Visual attention, Graph theory
- in
- MethodsX
- volume
- 12
- article number
- 102662
- publisher
- Elsevier
- external identifiers
-
- scopus:85189352809
- pmid:38577409
- ISSN
- 2215-0161
- DOI
- 10.1016/j.mex.2024.102662
- language
- English
- LU publication?
- no
- additional info
- © 2024 The Authors.
- id
- ebec0488-bf01-4b52-b1ca-76ad459df7a8
- date added to LUP
- 2024-10-15 08:43:50
- date last changed
- 2025-06-12 00:01:06
@article{ebec0488-bf01-4b52-b1ca-76ad459df7a8,
abstract = {{<p>This article provides a step-by-step guideline for measuring and analyzing visual attention in 3D virtual reality (VR) environments based on eye-tracking data. We propose a solution to the challenges of obtaining relevant eye-tracking information in a dynamic 3D virtual environment and calculating interpretable indicators of learning and social behavior. With a method called "gaze-ray casting," we simulated 3D-gaze movements to obtain information about the gazed objects. This information was used to create graphical models of visual attention, establishing attention networks. These networks represented participants' gaze transitions between different entities in the VR environment over time. Measures of centrality, distribution, and interconnectedness of the networks were calculated to describe the network structure. The measures, derived from graph theory, allowed for statistical inference testing and the interpretation of participants' visual attention in 3D VR environments. Our method provides useful insights when analyzing students' learning in a VR classroom, as reported in a corresponding evaluation article with N = 274 participants. <br/>•Guidelines on implementing gaze-ray casting in VR using the Unreal Engine and the HTC VIVE Pro Eye.<br/>•Creating gaze-based attention networks and analyzing their network structure.<br/>•Implementation tutorials and the Open Source software code are provided via OSF: https://osf.io/pxjrc/?view_only=1b6da45eb93e4f9eb7a138697b941198. </p>}},
author = {{Stark, Philipp and Hasenbein, Lisa and Kasneci, Enkelejda and Göllner, Richard}},
issn = {{2215-0161}},
keywords = {{Network analysis; Eye tracking; Virtual Reality; Visual attention; Graph theory}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{MethodsX}},
title = {{Gaze-based attention network analysis in a virtual reality classroom}},
url = {{http://dx.doi.org/10.1016/j.mex.2024.102662}},
doi = {{10.1016/j.mex.2024.102662}},
volume = {{12}},
year = {{2024}},
}