VR training program for fire escape: Learning progress predicted by the perception of fire presence, VR operational frustration, and gameplay self-efficacy

Autor: Jon-Chao Hong, Hsun-Yu Chan, Yun-Hsuang Teng, Kai-Hsin Tai, Chang-Zhen Lin
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Computers & Education: X Reality, Vol 3, Iss , Pp 100029- (2023)
Druh dokumentu: article
ISSN: 2949-6780
DOI: 10.1016/j.cexr.2023.100029
Popis: Most VR fire escape training programs only task learners to observe the procedure of fire escape in different simulated fire scenes. To improve the effectiveness of such training programs for everyone, we tested a “fire escape virtual reality training program” which takes advantage of the feedback on the action to help individuals to learn the necessary and correct steps of fire escape. The virtual program emulates a real fire scene by providing realistic visual and auditory stimuli. A single-group quasi-experimental study was carried out to measure the effectiveness of the program, and a total of 173 seventh- and eighth-grade students from a high school in New Taipei City participated. The results of structural equation modeling showed that 1) gameplay self-efficacy was negatively predicted by frustration, 2) fire presence positively predicted gameplay self-efficacy, and 3) gameplay self-efficacy positively predicted learning progress. The findings suggested that critical life-saving skills such as fire escape skills can be readily acquired and trained through individual virtual reality training programs.
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