Polar (DCL- 16-119): Collaborative Research: Computational Guided Inquiry for Incorporating Polar Research into Undergraduate Curricula
Polar (DCL- 16-119): Collaborative Research: Computational Guided Inquiry for Incorporating Polar Research into Undergraduate Curricula
批准号:
1712282
负责人:
Steven Neshyba
金额:
$13.78万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-06-01 至 2022-06-30
中文摘要
这个项目将探索当计算引导查询(CGI)被用来让本科生以有意义的方式体验极地研究时对学生学习的影响。在CGI结构的课程中,教师使用计算工具管理、分析和可视化数据,指导学生进行科学探究。该项目将创建一套七个CGI模块(例如,Jupyter笔记本、Excel电子表格),将极地研究和数据纳入各种本科生课程。其目的是提高学生的气候素养,提高学生使用真实数据进行科学探究的能力,同时提高课程目标的学习成果。教师将在暑期研讨会中接受在主动学习框架中部署CGI模块的培训,然后在各种本科课程(大气科学、化学、物理、环境经济学和计算机科学)中实施这些模块。将使用独立评估来探索学习结果。第二个工作坊将解决挑战并改进模块和教学材料,将在教育网站门户网站上传播。拟议的工作将提高我们对学生如何学习的理解,包括如何通过整合(1)使用真实世界的数据(2)课堂翻转的主动学习技术和(3)计算工具来改善学习结果。使用真实世界的数据集被认为能够真正地让学生参与到与他们相关的问题中来。CGI模块代表了一种新的课程工具,具有促进跨学科学习的潜力:学生在学习课程主题的同时还学习极地数据、如何在查询中使用真实数据以及计算机编程。这项拟议的工作是测试和评估这些CGI模块促进学生学习的潜力的第一步。拟议的工作代表了一系列更广泛的影响,包括本科生的教育、课程材料的开发、主动学习方法的改进和本科生参与研究。这项工作将通过增加气候知识、了解极地地区及其在气候中的作用以及在本科生水平上的计算能力来造福社会。此外,学生在这里提出的积极学习活动中获得的技能预计在课堂以外的环境中也是有用的。这些技能包括协作、批判性思维、数据分析和解决问题的技能,这些技能对于帮助学生学习科学地思考复杂挑战的工程解决方案至关重要;这些技能被认为对成功的STEM教育和内容本身一样关键。学生用真实的数据和真正的研究工具进行探究,将有助于他们将自我认知从被动的学习者转变为实现的科学家。作为这项提案的一部分开发的教材将通过聘请来自一系列机构的教师来直接影响各种本科课程,包括州立大学、文科学院和社区学院。这个项目有可能在助学金期间接触到大约1000名本科生。为了实现更广泛的传播,并在资助期结束后继续惠及本科生,所有材料将在一个在线教育门户网站上共享。
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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
Teaching modules for estimating climate change impacts in economics courses using computational guided inquiry
使用计算引导探究来估计经济学课程中气候变化影响的教学模块
DOI:
10.1080/00220485.2020.1731383
发表时间:
2020
期刊:
The Journal of Economic Education
影响因子:
--
作者:
[Fortmann, Lea, Beaudoin, Justin, Rajbhandari, Isha, Wright, Aedin, Neshyba, Steven, Rowe, Penny]
通讯作者:
Rowe, Penny
Collaborative Research: Polar (NSF 19-601): RUI: Computational Polar ENgagement through GUided INquiry (Computational PENGUIN)
-
批准号:2021213
-
项目类别:Standard Grant
-
资助金额:$9.59万
-
财政年份:2020
-
负责人:Steven Neshyba
-
依托单位:
RUI: Toward a Comprehensive Theory of Mesoscopic Morphology of Ice
-
批准号:1807898
-
项目类别:Standard Grant
-
资助金额:$27.0万
-
财政年份:2018
-
负责人:Steven Neshyba
-
依托单位:
RUI: Scanning electron microscopy and multiscale modeling of mesoscopically rough faceted ice
-
批准号:1306366
-
项目类别:Standard Grant
-
资助金额:$19.72万
-
财政年份:2013
-
负责人:Steven Neshyba
-
依托单位:
High Resolution Infrared Radiometry of the Arctic Sky from a Ship-based Platform
-
批准号:9712873
-
项目类别:Standard Grant
-
资助金额:$1.89万
-
财政年份:1997
-
负责人:Steven Neshyba
-
依托单位:
NATO Postdoctoral Fellow#
-
批准号:8953824
-
项目类别:Fellowship Award
-
资助金额:$4.11万
-
财政年份:1989
-
负责人:Steven Neshyba
-
依托单位:
国内基金
海外基金
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