Unlabelled: The series of evacuation drills implemented for flood disasters were mostly through monotonous traditional pedagogical methods. The application of these methods was observed to be ineffective because of the inability to realistically represent the actual dangers and have a significant impact on behavioural changes. Therefore, this research aimed to develop and test the effectiveness of Immersive Virtual Reality (IVR) Flood Evacuation to improve knowledge and self-efficacy regarding flood disasters. This was achieved through the adoption of a one-group pretest-posttest design as well as the selection of 45 students as participants. The results showed that IVR significantly improved the knowledge of the best evacuation practices and self-efficacy (-test, < 0.005) with long-term influence on the memory of the participants. These observations supported the previous studies that reported the ability of virtual reality in ensuring longer knowledge retention compared to traditional learning methods.
Contribution: Immersive virtual reality was found to have the potential to be applied as an interesting pedagogical tool for flood evacuation training. The application of the method for drills was discovered to be more efficient, cost-effective, and provide enhanced knowledge retention for users. This research shows the significance of seamlessly incorporating knowledge with flood evacuation practices through IVR in disaster education programmes. The integration is important in the transformation of knowledge into actionable steps, thereby enhancing overall preparedness.
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http://dx.doi.org/10.4102/jamba.v17i1.1655 | DOI Listing |
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Centre for Human Drug Research, Leiden, Netherlands.
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February 2025
Division of Orthopedic Surgery, Department of Surgery, University of Alberta, Edmonton, AB, Canada.
Research (Wash D C)
March 2025
The First Affiliated Hospital of Jinzhou Medical University, Jinzhou 121012, China.
The metaverse enables immersive virtual healthcare environments, presenting opportunities for enhanced care delivery. A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications, which is a problem that needs to be addressed. This paper proposes a novel multimodal learning framework for metaverse healthcare, MMLMH, based on collaborative intra- and intersample representation and adaptive fusion.
View Article and Find Full Text PDFFront Hum Neurosci
February 2025
Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios.
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School of Physical Education, Qingdao University, Qingdao, China.
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