Collaborative Research: Polar (NSF 19-601): RUI: Computational Polar ENgagement through GUided INquiry (Computational PENGUIN)

合作研究：极地 (NSF 19-601)：RUI：通过引导查询进行计算极地参与（计算企鹅）

• 批准号: 2021213
• 负责人: Steven Neshyba
• 金额: $ 9.59万
• 依托单位: University of Puget Sound
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021213&HistoricalAwards=false
• 关键词: Collaborative; Research; Polar; NSF; 19

Researchers from the Northwest Research Associates Inc. and the University of Puget Sound will explore the pedagogical benefits of using computational guided inquiry (CGI) to integrate products of contemporary polar research into undergraduate courses. In CGI-structured learning, the instructor guides students in hands-on scientific inquiry using computational tools for analysis and visualization of data. The PIs propose to develop twelve, 2-3 week undergraduate teaching modules based on the analysis of polar data. The modules will be tested in a variety of undergraduate classrooms, including those at two- and four-year institutions. Students will gain computational skills working with Python programming and with critical thinking and analysis of polar data sets. The proposed work offers a range of broader impacts, including education of undergraduate students, development of new course materials, advancement of active learning methods, and engagement of undergraduate student researchers in polar and pedagogical research. Benefits to society include increasing climate and computational literacy and the understanding of polar regions and their role in the climate. The educational materials developed as part of this proposal will directly impact a variety of undergraduate courses at a range of institutions, including state universities, liberal arts colleges, and community colleges. The project is designed to test whether working directly with polar data via CGI can improve undergraduate climate literacy while enhancing learning outcomes in a variety of undergraduate courses. The work builds on proven educational practices: active learning and guided inquiry. Working with CGI-experienced instructors who will teach the modules, undergraduate researchers and an education research specialist, the PIs will refine the modules, guided by a constructivist framework that includes development of a mental model and hypothesis formulation and testing. An external evaluator will develop questions to measure student knowledge gains and deploy pre- and post-module knowledge surveys. Student survey results will be analyzed to assess knowledge gains and attitudinal shifts. Dissemination to the wider educational community will be accomplished through webinars, conferences, and via the Science Education Resource Center (SERC) educational website portal. This project is funded by the Office of Polar Programs and the Division of Undergraduate Education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

来自西北研究协会公司和普吉特海湾大学的研究人员将探索使用计算机引导探究（CGI）将当代极地研究成果整合到本科课程中的教学效益。在cgi结构化的学习中，教师指导学生使用计算工具对数据进行分析和可视化，进行动手科学探究。pi建议基于极地数据分析开发12个2-3周的本科教学模块。这些模块将在包括两年制和四年制大学在内的各种本科教室中进行测试。学生将获得使用Python编程、批判性思维和极地数据集分析的计算技能。建议的工作提供了一系列更广泛的影响，包括本科生的教育，新课程材料的开发，主动学习方法的进步，以及本科生研究人员在极地和教学研究中的参与。对社会的好处包括提高气候和计算知识，以及对极地及其在气候中的作用的了解。作为该提案一部分开发的教育材料将直接影响一系列机构的各种本科课程，包括州立大学、文理学院和社区学院。该项目旨在测试通过CGI直接处理极地数据是否可以提高大学生的气候素养，同时提高各种本科课程的学习成果。这项工作建立在经过验证的教育实践之上：主动学习和引导探究。与cgi经验丰富的教师（将教授这些模块）、本科生研究人员和教育研究专家合作，pi将在一个建构主义框架的指导下完善这些模块，该框架包括心理模型的开发、假设的制定和检验。外部评估人员将开发问题来衡量学生的知识收获，并部署模块前和模块后的知识调查。学生调查结果将被分析，以评估知识的收获和态度的转变。将通过网络研讨会、会议和科学教育资源中心（SERC）教育网站门户向更广泛的教育界进行传播。该项目由极地项目办公室和本科教育司资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。