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

网络学习和未来学习技术计划为支持设想学习技术的未来并推进我们对人们如何在技术丰富的环境中学习的了解的努力提供资金。开发和实施（DIP）项目建立在概念验证工作的基础上，展示了拟议的新型学习技术的可能性，PI团队建立并完善了他们提出的创新的最低可行性示例，使他们能够理解未来应该如何设计和使用这种技术，并使他们能够回答有关人们如何学习，如何促进或评估学习，和/或如何设计学习。本课题是对一种新型的在线学习社区的构建和研究。WeatherBlur社区允许孩子，教师，科学家，渔民/渔民和社区成员一起学习和做与天气和气候对沿海社区的影响有关的科学。社区成员提出调查，收集和分享数据，共同学习。WeatherBlur被设计成一种新形式的知识构建社区，即非分层在线学习社区。与其他公民科学工作不同，它强调让社区的所有成员都能够提出和开展调查（而不仅仅是帮助收集由专家科学家或教师设计的调查数据）。先前的研究已经证明了WeatherBlur与其他公民科学学习社区的重要结构差异。该项目将使用社会网络分析和话语分析来衡量学习过程，以及个人意义映射和科学认识论和图形解释技能的嵌入式评估来检查结果。这些措施将被用来探索知识的建设过程和所需的支架，以支持他们，谈判的解释和调查的角色，以及知识的特点，推动这一谈判过程。 这项工作将使用一个迭代的基于设计的研究过程进行，其中先前运行的WeatherBlur网站将通过新的自动提示和通知系统进行增强，这些系统支持社区的非等级性质，以及嵌入评估提示的工具，这些提示将衡量参与者的数据解释技能和认识信念。指数随机图模型将用于分析社会网络分析数据，并测试有关社会结构和结果之间关系的假设。

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.