EFFECT OF AUGMENTED REALITY-BASED EDUCATION ON DISASTER PREPAREDNESS AMONG RURAL COMMUNITIES IN DISASTER-PRONE AREA, INDONESIA: A PILOT STUDY
Keywords:
Augmented reality, education, disaster, preparedness, communityAbstract
Background: Disaster preparedness initiatives primarily utilize three conventional training methods: classroom instruction, web-based training with pre-recorded material, and real-life drills and tabletop activities. Augmented reality offers a promising solution for enhancing disaster preparedness.
Aims: This study aimed to determine the effect of augmented reality-based education on disaster preparedness among rural communities in disaster-prone area, Indonesia.
Methods: This study was This study employed a quasi-experimental with control group and repeated measure design. Data was collected between June to November 2023. Assessment of study outcome was done before intervention (T0), immediately after intervention (T1), and 2 weeks after the intervention (T2). The sample in this study was adult aged above 18 years old, able to write and read, without any cognitive of mental disorder, having at least 6 months of experience as a cadre, and having a smartphone. Sample was recruited using a convenience sampling. Household emergency preparedness scale was used to measure variable. The analysis used ANOVA repeated test and general estimation equation (GEE).
Results: In the intervention group there was significant improvement of disaster preparedness from 2.56 (SD=1.22) to 4.22 (SD=1.23) at T2 with a modest effect size of 0.42. While in control group, no significant improvement showed at T2 (p>0.05). The findings of the GEE analysis revealed a statistically significant interaction between time and group in relation to disaster preparedness (ß = 11.3 (95% CI=5.22 – 14.34, p < 0.001).
Discussion: Augmented reality-based education has potential effect on enhancing individual disaster preparedness in rural communities in disaster-prone area West Java, Indonesia. Future studies are needed to confirm this finding using more robust design and larger sample size.
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