DIP: Next Generation WeatherBlur: Expanding Non-Hierarchical Online Learning Community Models for Citizen Science

Thursday, October 1, 2015 to Sunday, September 30, 2018
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Project Descriptions
Environment Type: 
Media and Technology, Websites, Mobile Apps, and Online Media
Elementary School Children (6-10) | Middle School Children (11-13) | Youth/Teen (up to 17) | General Public | Educators/Teachers | Scientists
Climate | Ecology, forestry, and agriculture | Education and learning science
Maine Mathematics and Science Alliance, Karen Peterman Consulting, Co.

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and PI teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This project is building and studying a new type of online learning community. The WeatherBlur community allows kids, teachers, scientists, fishermen/fisherwomen, and community members to learn and do science together related to the local impacts of weather and climate on their coastal communities. Members of the community propose investigations, collect and share data, and learn together. WeatherBlur is designed to be a new form of knowledge-building community, the Non-Hierarchical Online Learning Community. Unlike other citizen science efforts, there is an emphasis on having all members of the community able to propose and carry out investigations (and not just help collect data for investigations designed by expert scientists or teachers). Prior research has demonstrated important structural differences in WeatherBlur from other citizen science learning communities. The project will use social network analysis and discourse analysis to measure learning processes, and Personal Meaning Mapping and embedded assessments of science epistemology and graph interpretation skills to examine outcomes. The measures will be used to explore knowledge-building processes and the scaffolds required to support them, the negotiation of explanations and investigations across roles, and the epistemic features that drive this negotiation process. The work will be conducted using an iterative design-based research process in which the prior functioning WeatherBlur site will be enhanced with new automated prompt and notification systems that support the non-hierarchical nature of the community, as well as tools to embed assessment prompts that will gauge participants' data interpretation skills and epistemic beliefs. Exponential random graph modeling will be used to analyze the social network analysis data and test hypotheses about the relationship between social structures and outcomes.

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Team Members

Ruth Kermish-AllenRuth Kermish-AllenPrincipal Investigator
Christine BevcChristine BevcCo-Principal Investigator
Karen PetermanCo-Principal Investigator

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