Challenges and opportunities of disaster education program among UUM student

Lin, Chong Khai; Nifa, Faizatul Akmar Abdul; Musa, Suria; Shahron, Syairah Aimi; Anuar, Nur Anisah

doi:10.1063/1.5055440

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…It is also available for effective use of satellite positioning, remote sensing and GIS, for disaster monitoring and management. Dealing only with natural disasters, Internet GIS for Crisis Management as Approaches Methods [23] Summarizy situational tweets in crisis events: An extractiveabstractive approach [35] Educational Purpose of the Faculty of Higher Education with Special Reference [1] Game-based Learning of Flood Disasters [26] The importance of education on disasters and emergencies [25] Challenges and opportunities of education programs [27] A tabletop simulation system for disaster education [28] Flood protection computer game for disaster education [29] Using immersive game-based virtual reality to teach fire-safety skills to children [30] Penumbral Tourism: Place-based Disaster Education via Realworld Disaster Simulation [31] A mixed reality game to investigate coordination in disaster response [3] FeedForward The neural network-based disaster alert models [2] is one of the first to use multiple sources, namely Twitter and Facebook, for capturing messages during a crisis. Followed by the smart interface-based automated learning environment to improve [3] disaster warning, while introducing smart education.…”

Section: Related Workmentioning

confidence: 99%

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Bouzidi

Boudries

Amad

2022

Preprint

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Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections. This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability.

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Section: Related Workmentioning

confidence: 99%

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Bouzidi

Boudries

Amad

2022

Preprint

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“…In the literature, studies on natural disasters in the field of social sciences focus on disaster education (de Mendonca & Valois, 2017;Lin et al, 2018;Nifa et al, 2017;Panić et al, 2013;Sözcü, 2020) and disaster literacy (Çalışkan, 2020;Kimura et al, 2017;Priyowidodo & Luik, 2013;Sözcü & Aydınözü, 2019). Some studies on disaster education address the aforementioned interdisciplinary approach.…”

Section: Literature Reviewmentioning

confidence: 99%

The Design and Implementation of a Teacher Training Program for Improving Teachers' Disaster Literacy: Interdisciplinary Disaster Education Program (Idep)

Şeyihoğlu

Kartal

Tekbıyık

et al. 2021

PEC

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The interdisciplinary aspect of natural disasters requires their correlation with more than one discipline and orientation for understanding and teaching the inevitable nature of disasters. This study aimed to design a teacher training program called the Interdisciplinary Disaster Education Program (IDEP) and reveal its effect on improving teachers' natural disaster literacy. Furthermore, teachers' opinions about program were examined. The mixed-method design was used in the study. The study participants consisted of 36 teachers (science, classroom, and social studies teachers) working on different subjects. Data were obtained using the Natural Disaster Literacy Scale and the survey for the IDEP. The data collected from the scale were analyzed using two-way mixed ANOVA. The qualitative data collected from the survey were subjected to content analysis. The results indicated that the IDEP statistically improved teachers' natural disaster literacy. Moreover, the survey results highlighted that different instructional activities (experiments, modeling, workshops, rock/soil analysis, coding, augmented reality, and STEM applications, etc.) related to natural disasters contributed to a lot of professional and personal knowledge of teachers, particularly disaster management. Keywords: disaster literacy, interdisciplinary education, mixed method, natural disaster, teacher training program

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