EAGER: WeatherBlur

网络学习和未来学习技术计划资助的工作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。支持学习的一个有前途的方法是使用在线学习社区。另一种方法是基于地方的教育，在这种教育中，人们通过在他们熟悉的地方（他们的家、城镇和地区）应用概念来学习概念。该项目考察了一个名为WeatherBlur的现有实验性在线社区，该社区使用两种方法和公民科学将儿童、教师、渔民和妇女以及科学家联系起来，以了解阿拉斯加和缅因州天气和气候对当地的影响。该项目将研究这个独特的社区如何将人们联系起来，并将收集其他学习社区的数据，以比较社区的哪些特征使人们能够在如此不同的受众中进行合作和学习。这项研究将为如何有效地构建像WeatherBlur这样的在线社区提供一套指导原则。本项目旨在通过探索非层级学习社群理论，为计算机支持的协同学习（CSCL）理论做出贡献。将从WeatherBlur社区收集数据，包括社交网络数据和一系列利益相关者访谈，以记录WeatherBlur内部的当前实践；这些数据将被归纳地用来帮助阐述理论。涉众的感知和理论构建将通过至少两次成员检查的迭代而相互和迭代。然后，该项目将对WeatherBlur社区和其他公民科学在线社区进行跨案例比较分析，以帮助描述不同类型的学习社区之间的关系。跨案例分析将依赖于自我报告调查和与社区架构师和社区参与者的半结构化访谈。此外，我们将构建一个社区观察协议来实现非分层学习社区概念，并将用于丰富跨案例比较。

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. One promising approach to support learning is the use of online learning communities. Another approach is place-based education, in which people learn about concepts through their application in the places people know: their homes, towns, and regions. This project examines an existing experimental online community called WeatherBlur, which uses both approaches as well as citizen science to connect kids, teachers, fishermen and -women, and scientists to learn about the local impacts of weather and climate in Alaska and Maine. The project will examine how this unique community connects people and will collect data on other learning communities to compare what features of the community allow people to collaborate and learn well across such diverse audiences. The research will yield a set of guiding principles for how to effectively structure online communities like WeatherBlur.This project aims to contribute to theories of computer-supported collaborative learning (CSCL) by exploring the theory of non-hierarchical learning communities. Data will be collected from the WeatherBlur community including social network data and a series of stakeholder interviews to document current practices within WeatherBlur; this data will be used inductively to help elaborate the theory. Stakeholder perceptions and theory building will be reciprocal and iterative through at least two iterations of member checks. Then, the project will conduct a cross-case comparative analysis of the WeatherBlur community and other citizen science online communities to help characterize the relationship between different types of communities for learning. The cross-case analysis will rely on self-report surveys and semi-structured interviews with both community architects and community participants. Furthermore, a community observation protocol will be constructed to operationalize the non-hierarchical learning community concept, and will be used to enrich the cross-case comparison.