Polar (DCL- 16-119): Collaborative Research: Computational Guided Inquiry for Incorporating Polar Research into Undergraduate Curricula

GSTDTAP

项目编号	1712354
	Polar (DCL- 16-119): Collaborative Research: Computational Guided Inquiry for Incorporating Polar Research into Undergraduate Curricula
	Penny Rowe
主持机构	NorthWest Research Associates, Incorporated
项目开始年	2017
	2017-06-01
项目结束日期	2019-05-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	159890(USD)
国家	美国
语种	英语
英文摘要	This project will explore impacts on student learning when computational guided inquiry (CGI) is used to allow undergraduate students to experience polar research in a meaningful way. In a CGI-structured course, the instructor guides the students in scientific inquiry using computational tools for managing, analyzing, and visualizing data. This project will create a set of seven CGI modules (e,g, Jupyter notebooks, Excel spreadsheets) that will incorporate polar research and data into a variety of undergraduate classes. The aim is to improve student climate literacy and to increase student ability to use real data to conduct scientific inquiry while at the same time enhancing learning outcomes for course objectives. Instructors will be trained in deploying the CGI modules in an active learning framework in a summer workshop and will then implement the modules in a variety of undergraduate classes (atmosphere science, chemistry, physics, environmental economics, and computer science). Independent evaluation will be used to explore learning outcomes. A second workshop will address challenges and lead to improved modules and instructional material that will be disseminated on an educational website portal. The proposed work will improve our understanding of how students learn, including how learning outcomes can be improved through integration of (1) use of real-world data (2) the active-learning technique of classroom flipping and (3) computational tools. Use of real-world datasets is believed to authentically engage students in questions that are relevant to them. The CGI modules represent a novel curricular tool, which has the potential to foster cross-disciplinary learning: students learn course topics while also learning about polar data, how to use real data in inquiry, and computer programming. The proposed work is a first step in testing and evaluating the potential of these CGI modules to enhance student learning. The proposed work represents a range of broader impacts, including education of undergraduate students, development of course materials, advancement of active learning methods and undergraduate student participation in research. This work will benefit society through increasing climate literacy, understanding of the Polar Regions and their role in the climate, and computational literacy at the undergraduate level. Furthermore, the skills acquired by students engaging in the active learning activities proposed here are expected to be useful in contexts beyond the classroom. These include collaboration, critical thinking, data analysis, and problem solving skills, which are vital in helping students learn to think scientifically about engineering solutions to complex challenges; these skills are believed to be as critical to successful STEM education as the content itself. Student engagement in inquiry with real data and bona fide research tools will help change their self-perceptions from passive learners to realized scientists. The educational materials developed as part of this proposal will directly impact a variety of undergraduate courses through the engagement of instructors from a range of institutions, including state universities, liberal arts colleges, and community colleges. This project has the potential of reaching an estimated 1000 undergraduate students during the grant period. To achieve wider dissemination, and to continue to reach undergraduate students after the grant period ends, all materials will be shared in an online educational portal.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71096
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Penny Rowe.Polar (DCL- 16-119): Collaborative Research: Computational Guided Inquiry for Incorporating Polar Research into Undergraduate Curricula.2017.