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展望学习技术的未来并推动我们了解人们如何在技术丰富的环境中学习的努力提供资金。开发和实施(DIP)项目建立在概念验证工作的基础上，这些工作展示了拟议的新型学习技术的可能性，而PI团队建立和完善了其提议的创新的最低可行范例，使他们能够了解未来应如何设计和使用这种技术，并使他们能够回答关于人们如何学习、如何促进或评估学习、和/或如何为学习而设计的问题。该项目正在建设和研究一种新型的在线学习社区。WeatherBlur社区允许儿童、教师、科学家、渔民/渔民和社区成员一起学习和研究与当地天气和气候对其沿海社区的影响有关的科学。社区成员提出调查建议，收集和共享数据，并共同学习。WeatherBlur旨在成为一种新型的知识构建社区--无等级在线学习社区。与其他公民科学努力不同，它强调让社区的所有成员都能够提出和进行调查(而不仅仅是帮助为专家科学家或教师设计的调查收集数据)。先前的研究表明，WeatherBlur与其他公民科学学习社区存在重要的结构性差异。该项目将使用社会网络分析和话语分析来衡量学习过程，并使用个人意义图谱和科学认识论和图表解释技能的嵌入式评估来检查结果。这些措施将用于探讨知识建设进程和支持这些进程所需的脚手架、跨角色的解释和调查谈判以及推动这一谈判进程的认知特征。这项工作将使用基于设计的迭代研究过程进行，在该过程中，将用新的自动提示和通知系统来加强先前运行的WeatherBlur网站，这些系统支持社区的非层级性质，以及嵌入评估提示的工具，这些工具将评估参与者的数据解释技能和认知信念。将使用指数随机图建模来分析社会网络分析数据，并检验关于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.