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Assessing Knowledge Retention of an Immersive Serious Game vs. a Traditional Education Method in Aviation Safety
Abstract
Thanks to the increasing availability of consumer head-mounted displays, educational applications of immersive VR could now reach to the general public, especially if they include gaming elements (immersive serious games). Safety education of citizens could be a particularly promising domain for immersive serious games, because people tend not to pay attention to and benefit from current safety materials. In this paper, we propose an HMD-based immersive game for educating passengers about aviation safety that allows players to experience a serious aircraft emergency with the goal of surviving it. We compare the proposed approach to a traditional aviation safety education method (the safety card) used by airlines. Unlike most studies of VR for safety knowledge acquisition, we do not focus only on assessing learning immediately after the experience but we extend our attention to knowledge retention over a longer time span. This is a fundamental requirement, because people need to retain safety procedures in order to apply them when faced with danger. A knowledge test administered before, immediately after and one week after the experimental condition showed that the immersive serious game was superior to the safety card. Moreover, subjective as well as physiological measurements employed in the study showed that the immersive serious game was more engaging and fear-arousing than the safety card, a factor that can contribute to explain the obtained superior retention, as we discuss in the paper.
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“Chittaro and Buttussi (2015) likewise showed that participants using an immersive VR fl ight safety simulation retained signi fi cantly more knowledge than those reading a safety card with similar instructions.”
From Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment (2019) · cites this paper as Chittaro and Buttussi (2015)
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Metadata
- Authors
- Chittaro, L., & Buttussi, F.
- Affiliations
- University of Udine (IT)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2015-04-18
- Volume
- 21
- Issue
- 4
- Pages
- 529-538
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W2058984179
- Cited by
- 373
- References
- 54
- Citations by year
- 2026: 27 · 2025: 32 · 2024: 36 · 2023: 48 · 2022: 45 · 2021: 51 · 2020: 40 · 2019: 32 · 2018: 27 · 2017: 19 · 2016: 12 · 2015: 4
- FWCI
- 39.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System (University of Udine) (submittedVersion)
- Funders
- Federal Aviation Administration
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Keywords
- virtual reality, aviation safety education, knowledge retention, head-mounted displays, fear arousal, engagement, emergency simulation
- MeSH terms
- Accidents, Aviation, Aviation, Computer Graphics, Humans, User-Computer Interface, Video Games
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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