Integrating Computational Science Practice, Weather Data Analysis, and 3D Visualization in the Secondary Earth and Environmental Science Curriculum

将计算科学实践、天气数据分析和 3D 可视化融入中学地球与环境科学课程

基本信息

批准号：
1934194
负责人：
Yan Sun
金额：
$ 163.22万
依托单位：
Mississippi State University
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：
2020-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934194&HistoricalAwards=false
关键词：
Integrating Computational Science Practice Weather

项目摘要

As computing has become integral to the practice of science, technology, engineering and mathematics (STEM), the STEM+Computing program seeks to address challenges in computational STEM areas through the applied integration of computational thinking and computing activities within STEM teaching and learning in early childhood education through high school. Scientific inquiry often requires scientists to use large datasets and computational models to advance knowledge about the structure and functions of complex systems, and to predict changes in those systems. Middle and high school students will be engaged in these scientific processes by infusing the computational practices and thinking needed to model, visualize, and communicate atmospheric processes and changes into science teaching and learning. The project will design, develop, and test eight learning modules for middle and high school students that integrate computational practices and thinking with atmospheric science through use of data analysis, visualization, and modeling of large-scale weather datasets. Each of the modules will engage students in using a free, open-source application for analyzing and visualizing geoscience data. Each module will also emphasize the following computational science concepts and practices: 1) The ability to access and manipulate data, 2) The ability to use computational tools to analyze and interpret data, and 3) The application of computational reasoning and model-based understanding to construct quantitative, scientific explanations and predictions about events and processes in atmospheric systems. Teachers will be participate in developing and testing new approaches to teaching an learning, as well as development of a framework for supporting ongoing teacher efforts to create new instructional materials that integrate computational thinking science practices. This project will use a design-based research approach to test the hypothesis that science education empowered by large-scale atmospheric datasets and fused with computational thinking and practices will: 1) promote meaningful science learning as envisioned by the Next Generation Science Standards, and 2) foster literacy in atmospheric and computational sciences among middle and high school students. The project team includes academic specialists in instructional systems and workforce development, geoscience, electrical and computer engineering, and a high-performance computer collaboratory, and this team will collaborate with schoolteachers from eight school districts to develop, test, and implement the new learning modules. The project will directly engage approximately 44 secondary school teachers and 2,000 of their students. The work of the project is guided by five objectives: 1) Develop and test 3D Weather learning modules that integrate computational thinking and practices into atmospheric science learning through data analysis, and 3D visualization and interpretation of large-scale weather data; 2) Develop and conduct teacher professional development that supports integration of computational thinking and practices into secondary science instruction and empowers secondary science learning with large-scale weather data; 3) Investigate how the integration of 3D Weather data modeling and visualization into Earth and environmental science classes support students' and teachers' development of computational thinking and model-based understanding of the complexity of the atmospheric systems; 4) Investigate teachers' experiences and perceptions of integrating computational thinking and practices into atmospheric science instruction in order to learn how to support teachers to engage in these instructional practices, and 5) Develop a research-based framework guiding teachers' future efforts of developing new instructional materials integrating computational thinking and practices into science instruction. Research activities related to each of these objectives will be employed to guide the iterative process of improving educational practices through analysis, design, development, and implementation.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.

随着计算已经成为科学，技术，工程和数学（STEM）实践不可或缺的一部分，STEM+计算计划旨在通过应用计算思维和计算活动在幼儿教育中通过高中进行STEM教学中的计算思维和计算活动来应对计算STEM领域的挑战。科学询问通常要求科学家使用大型数据集和计算模型来促进有关复杂系统的结构和功能的知识，并预测这些系统的变化。中学和高中生将通过注入建模，可视化和交流大气过程并变化为科学教学和学习所需的计算实践和思维来参与这些科学过程。该项目将通过使用数据分析，可视化和大规模天气数据集建模，为中学和高中生设计，开发和测试中学和高中生的八个学习模块。每个模块都将吸引学生使用免费的开源应用程序来分析和可视化地球科学数据。每个模块还将强调以下计算科学概念和实践：1）访问和操纵数据的能力，2）使用计算工具分析和解释数据的能力，以及3）计算推理和基于模型的理解以构建定量，科学的解释和预测大气系统中事件和过程的应用。教师将参与开发和测试新的教学方法，以及开发一个框架，以支持持续的教师努力创建新的教学材料，以整合计算思维科学实践。该项目将使用基于设计的研究方法来检验以下假设：大气数据集赋予并与计算思维和实践融合的科学教育将：1）促进下一代科学标准所设想的有意义的科学学习，以及2）培养中学和高中生大气和计算科学的素养。该项目团队包括教学系统和劳动力发展，地球科学，电气和计算机工程的学术专家，以及高性能的计算机协作，该团队将与来自八个学区的学校老师合作，开发，测试和实施新的学习模式。该项目将直接与大约44名中学教师和2,000名学生互动。该项目的工作由五个目标指导：1）开发和测试3D天气学习模块，这些模块通过数据分析将计算思维和实践整合到大气科学学习中，以及3D可视化和大规模天气数据的解释； 2）开发和进行教师专业发展，以支持将计算思维和实践整合到中学科学教学中，并通过大规模的天气数据使二级科学学习能力； 3）研究3D天气数据建模和可视化与地球和环境科学课程的整合如何支持学生和教师的计算思维的发展以及基于模型的大气系统复杂性的理解； 4）调查教师对将计算思维和实践整合到大气科学教学中的看法，以学习如何支持教师参与这些教学实践，以及5）开发基于研究的框架指导教师的未来框架，以开发新的教学材料，将计算思维的新教学材料整合到科学教学中。将采用与这些目标相关的研究活动，以通过分析，设计，开发和实施来指导改善教育实践的迭代过程。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估来通过评估来支持的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Preparing Teachers to Teach Computational Thinking with 3D Weather Data Visualization

让教师做好利用 3D 天气数据可视化教授计算思维的准备

DOI：
发表时间：
2021
期刊：
& Education
影响因子：
0
作者：
Sun, Y.
通讯作者：
Sun, Y.

3D weather data visualization with IDV: Computational thinking contextualized in atmospheric science

使用 IDV 进行 3D 天气数据可视化：大气科学背景下的计算思维

DOI：
发表时间：
2021
期刊：
2021 Association for Educational Communications & Technology International Convention
影响因子：
0
作者：
Sun, Y.
通讯作者：
Sun, Y.

Using IDV to promote computational thinking in atmospheric science learning

利用 IDV 促进大气科学学习中的计算思维

DOI：
发表时间：
2021
期刊：
2021 Association for Educational Communications & Technology International Convention
影响因子：
0
作者：
Sun, Y.
通讯作者：
Sun, Y.

Work-in-Progress: Incorporating computational thinking instruction into K-12 using 3D weather

正在进行的工作：使用 3D 天气将计算思维教学纳入 K-12

DOI：
发表时间：
2021
期刊：
2021 ASEE Virtual Annual Conference
影响因子：
0
作者：
Ko, P.
通讯作者：
Ko, P.

Preparing teachers to teach spatial computational thinking with IDV visualization of weather data

帮助教师利用 IDV 天气数据可视化教授空间计算思维

DOI：
发表时间：
2022
期刊：
Preparing teachers to teach spatial computational thinking with IDV visualization of weather data
影响因子：
0
作者：
Sun, Yan;Dyer, Jamie;Mohammadi-Aragh, Jean;Harris, Jonathan
通讯作者：
Harris, Jonathan

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Yan Sun其他文献

Discovery of new thieno[2,3-d]pyrimidine and thiazolo[5,4-d]pyrimidine derivatives as orally active phosphoinositide 3-kinase inhibitors.

发现新的噻吩并[2,3-d]嘧啶和噻唑并[5,4-d]嘧啶衍生物作为口服活性磷酸肌醇3-激酶抑制剂。

DOI：
发表时间：
2020
期刊：
Bioorganic & Medicinal Chemistry
影响因子：
3.5
作者：
Yan Sun;Ronggeng Fu;Songwen Lin;Jingbo Zhang;M. Ji;Yan Y. Zhang;Deyu Wu;Kehui Zhang;Hua Tian;Mingyi Zhang;L. Sheng;Yan Li;Jing Jin;Xiaoguang Chen;Heng Xu
通讯作者：
Heng Xu

The Effects of Collectivism-Individualism on the Cooperative Learning of Motor Skill

集体主义-个人主义对运动技能合作学习的影响

DOI：
发表时间：
2013
期刊：
影响因子：
0
作者：
Yi Luo;Yan Sun;Johannes Strobel
通讯作者：
Johannes Strobel

Non-vanishing Berry curvature driven large anomalous Hall effect in non-collinear antiferromagnet Mn3Ge

非共线反铁磁体 Mn3Ge 中非零贝里曲率驱动的大反常霍尔效应

DOI：
发表时间：
2015
期刊：
影响因子：
0
作者：
A. Nayak;J. Fischer;Yan Sun;Binghai Yan;J. Karel;A. Komarek;C. Shekhar;Nitesh Kumar;W. Schnelle;J. Kuebler;S. Parkin;C. Felser
通讯作者：
C. Felser