RAPID: Collaborative Research: Learning about Infectious Diseases through Online Participation in a Virtual Epidemic
RAPID:协作研究:通过在线参与虚拟流行病来了解传染病
基本信息
- 批准号:1508981
- 负责人:
- 金额:$ 2.08万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-09-01 至 2017-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Epidemics (whether measles, ebola, or the flu) affect youth, but many of the concepts related to how epidemics work (and how to prevent them) are hard to convey to young learners. This project explores how and whether technology can help youth ages 10-14 years learn about how disease epidemics spread and how to prevent them by participating in and experiencing a virtual epidemic outbreak in a massive online community. The project will also provide data to epidemiologists to help understand how educating kids might change the course of epidemics.This project uses prominent features of virtual worlds--persistence, real time, personal representation, and massive numbers of players--to allow tweens to experience and investigate an epidemic outbreak. A virtual epidemic will be 'unleashed' on a widely used online virtual learning space, Whyville, simulating important aspects of disease including movement of people and contact in social networks, incubation periods, exposure and exposure mitigation, and the utility of presymptomatic testing. Using an innovative combination of observational methods and field experiments, this pilot research will provide insights into how we can design such large-scale online activities to promote individual and community inquiry. The research will explore how public health prevention and protection measures are connected to behavioral changes, conceptual understanding of infection, immunity, and associated social issues. Measures will include youth behaviors online in the virtual world (including chat and logfile analysis, use of the virtual 'protective gear' or 'hand washing' features, and engagement with outbreak-related materials online, including live chats with an epidemiologist.)
网络学习和未来学习技术计划资助的努力将有助于设想下一代学习技术,并推进我们对人们如何在技术丰富的环境中学习的了解。流行病(无论是麻疹、埃博拉还是流感)都会影响年轻人,但许多与流行病如何发生(以及如何预防)有关的概念很难传达给年轻的学习者。该项目探讨了技术如何以及是否可以帮助10-14岁的青少年了解疾病流行病如何传播,以及如何通过参与和体验大规模在线社区中的虚拟流行病爆发来预防。该项目还将为流行病学家提供数据,帮助他们了解教育孩子可能如何改变流行病的进程。该项目利用虚拟世界的突出特点-持久性,真实的时间,个人代表性和大量玩家-让青少年体验和调查流行病爆发。一种虚拟的流行病将在一个广泛使用的在线虚拟学习空间Whyville上“释放”,模拟疾病的重要方面,包括社交网络中的人员流动和接触、潜伏期、暴露和暴露缓解以及症状前测试的效用。使用观察方法和实地实验的创新组合,这项试点研究将为我们如何设计这种大规模的在线活动以促进个人和社区调查提供见解。该研究将探讨公共卫生预防和保护措施如何与行为变化,对感染,免疫力和相关社会问题的概念理解联系起来。措施将包括青少年在虚拟世界中的在线行为(包括聊天和日志文件分析,使用虚拟的“防护装备”或“洗手”功能,以及在线参与与疫情相关的材料,包括与流行病学家的实时聊天。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Nina Fefferman其他文献
Vital rate sensitivity analysis as a tool for assessing management actions for the desert tortoise
- DOI:
10.1016/j.biocon.2009.06.025 - 发表时间:
2009-11-01 - 期刊:
- 影响因子:
- 作者:
J. Michael Reed;Nina Fefferman;Roy C. Averill-Murray - 通讯作者:
Roy C. Averill-Murray
DialectDecoder: Human/machine teaming for bird song classification and anomaly detection
DialectDecoder:人机协作进行鸟鸣分类和异常检测
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:5.1
- 作者:
Brittany Story;Patrick Gillespie;Graham Derryberry;Elizabeth Derryberry;Nina Fefferman;Vasileios Maroulas - 通讯作者:
Vasileios Maroulas
Nina Fefferman的其他文献
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{{ truncateString('Nina Fefferman', 18)}}的其他基金
PIPP Phase I: Predicting Emergence in Multidisciplinary Pandemic Tipping-points (PREEMPT)
PIPP 第一阶段:预测多学科流行病临界点的出现 (PREEMPT)
- 批准号:
2200140 - 财政年份:2022
- 资助金额:
$ 2.08万 - 项目类别:
Standard Grant
Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems
协作研究:多尺度、复杂、动态系统中出现前和罕见事件的预测研讨会
- 批准号:
2114651 - 财政年份:2021
- 资助金额:
$ 2.08万 - 项目类别:
Standard Grant
RAPID: Modeling the Coupled Social and Epidemiological Networks that Determine the Success of Behavioral Interventions on Limiting Spread of COVID-19
RAPID:对耦合的社会和流行病学网络进行建模,该网络决定限制 COVID-19 传播的行为干预措施是否成功
- 批准号:
2028710 - 财政年份:2020
- 资助金额:
$ 2.08万 - 项目类别:
Standard Grant
RAPID: Modeling Zika Control Effectiveness with Feedback in Risk Perception and Associated Demand across Scales of Intervention
RAPID:通过风险感知反馈和跨干预规模的相关需求来建模寨卡控制有效性
- 批准号:
1640951 - 财政年份:2016
- 资助金额:
$ 2.08万 - 项目类别:
Standard Grant
EAGER: Collaborative: Algorithmic Framework for Anomaly Detection in Interdependent Networks
EAGER:协作:相互依赖网络中异常检测的算法框架
- 批准号:
1646890 - 财政年份:2016
- 资助金额:
$ 2.08万 - 项目类别:
Standard Grant
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