EAGER: WeatherBlur

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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.