CAP: Innovating Data-driven Methodologies for Documenting and Studying Informal Learning

CAP：用于记录和研究非正式学习的创新数据驱动方法

• 批准号: 1457431
• 负责人: Leilah Lyons
• 金额: $ 10万
• 依托单位: New York Hall of Science
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457431&HistoricalAwards=false
• 关键词: CAP; Innovating; Data; driven; Methodologies

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. Capacity-building (CAP) projects increase the ability of researchers to understand how such technology should be designed and used in the future and supports new capacity in allowing researchers to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This project supports a workshop in which experts in museums, informal learning, complex systems, and data science collaborate with technologists to examine what types of technologies could help track how learners behave, and learn, in museums and other informal learning locations. The workshops would lead to a written document summarizing what is known about how to track learners for these purposes, and would help spark new collaborations leading to new approaches to these problems. Ethics would be a core theme, and experts in ethics would help ensure that all approaches explored would respect the privacy of museumgoers and other learners. The intellectual merit of this project is to advance the state of the art on how we track what learners do in museums, and the broader impact would be to make it easier to design museums and other learning environments to support learners, and to allow more rigorous assessment of learning behavior in museums.This project will conduct a three-day workshop leading to an edited volume on technology-enabled visitor metrics in museums and other science and technology centers. The leadership team includes expertise in learning sciences, computer science, interaction design, complex systems, and informal science education, and the workshop will additionally be supported by an advisory committee including experts in educational performance assessment and psychometrics, learning analytics, cross-cultural and cross-setting sociocultural learning theory, and educational policy. Workshop invitees will be recruited to include participants from several categories, including learning sciences, data sciences and learning analytics, informal science education and museum design, ubiquitous and pervasive computing, and data ethics. An embedded evaluation will interview participants after the workshops to gauge whether the meeting spurred new research collaborations.

网络学习和未来学习技术计划资助的努力将有助于展望下一代学习技术，并促进我们对人们在技术丰富的环境中如何学习的了解。能力建设(CAP)项目提高了研究人员理解未来应如何设计和使用这种技术的能力，并支持新的能力，使研究人员能够回答有关人们如何学习、如何促进或评估学习、和/或如何为学习而设计的问题。该项目支持一个研讨会，在研讨会上，博物馆、非正式学习、复杂系统和数据科学的专家与技术专家合作，研究哪些类型的技术可以帮助跟踪学习者在博物馆和其他非正式学习地点的行为和学习情况。研讨会将产生一份书面文件，总结关于如何为这些目的跟踪学习者的已知情况，并将有助于激发新的合作，从而产生解决这些问题的新方法。道德操守将是一个核心主题，道德操守专家将帮助确保所探讨的所有办法都尊重博物馆参观者和其他学习者的隐私。这个项目的智力价值是推动我们如何跟踪学习者在博物馆里做什么的最新技术，更广泛的影响将是使设计博物馆和其他学习环境来支持学习者变得更容易，并允许对博物馆中的学习行为进行更严格的评估。该项目将进行为期三天的研讨会，最终编辑博物馆和其他科技中心的技术支持的参观者指标。领导团队包括学习科学、计算机科学、交互设计、复杂系统和非正式科学教育方面的专业知识，研讨会还将得到一个咨询委员会的支持，该委员会成员包括教育绩效评估和心理测量学、学习分析、跨文化和跨环境的社会文化学习理论和教育政策方面的专家。讲习班的受邀者将包括来自几个类别的参与者，包括学习科学、数据科学和学习分析、非正式科学教育和博物馆设计、无处不在和普遍存在的计算以及数据伦理。研讨会结束后，一项嵌入式评估将采访参与者，以衡量会议是否刺激了新的研究合作。