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

随着计算成为科学、技术、工程和数学(STEM)实践中不可或缺的一部分，STEM计算计划寻求通过将计算思维和计算活动应用于STEM教与学的应用集成来应对计算STEM领域的挑战。科学研究通常要求科学家使用大数据集和计算模型来推进关于复杂系统结构和功能的知识，并预测这些系统的变化。初中生和高中生将通过将建模、可视化和交流大气过程和变化所需的计算实践和思维注入到科学教学和学习中，从而参与这些科学过程。该项目将为初中生设计、开发和测试八个学习模块，通过使用大规模天气数据集的数据分析、可视化和建模，将计算实践和思维与大气科学相结合。每个模块都将让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。每个单元还将强调以下计算科学概念和实践：1)访问和处理数据的能力，2)使用计算工具分析和解释数据的能力，以及3)应用计算推理和基于模型的理解，对大气系统中的事件和过程进行定量、科学的解释和预测。教师将参与开发和测试教授学习的新方法，以及支持教师正在进行的努力的框架的开发，以创建结合计算思维科学实践的新教材。该项目将使用一种基于设计的研究方法来检验这样一种假设，即由大规模大气数据集支持并与计算思维和实践相融合的科学教育将：1)促进下一代科学标准所设想的有意义的科学学习，2)培养中学生在大气和计算科学方面的素养。该项目团队包括教学系统和劳动力发展、地球科学、电气和计算机工程以及高性能计算机协作室方面的学术专家，该团队将与来自八个学区的学校教师合作开发、测试和实施新的学习模块。该项目将直接雇用大约44名中学教师和2000名学生。该项目的工作以五个目标为指导：1)开发和测试3D天气学习模块，通过数据分析和3D可视化和大规模天气数据的解释将计算思维和实践整合到大气科学学习中；2)开发和进行教师专业发展，支持将计算思维和实践整合到中学科学教学中，并支持利用大规模天气数据进行中学科学学习；3)调查将3D天气数据建模和可视化整合到地球和环境科学课堂如何支持学生和教师对大气系统复杂性的计算思维和基于模型的理解；4)调查教师将计算思维和实践融入大气科学教学的经验和看法，以了解如何支持教师从事这些教学实践；5)建立一个研究型框架，指导教师未来努力开发将计算思维和实践融入科学教学的新教材。与这些目标相关的研究活动将被用来指导通过分析、设计、开发和实施来改进教育实践的迭代过程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.