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

由于计算已经成为科学、技术、工程和数学（STEM）实践中不可或缺的一部分，STEM+计算计划旨在通过将计算思维和计算活动应用于从幼儿教育到高中的STEM教学和学习中，来解决计算STEM领域的挑战。科学探究通常需要科学家使用大型数据集和计算模型来推进对复杂系统的结构和功能的了解，并预测这些系统的变化。初中和高中学生将参与到这些科学过程中，通过将计算实践和思维融入到科学教学和学习中，以建模、可视化和交流大气过程和变化。该项目将为初高中学生设计、开发和测试8个学习模块，通过使用数据分析、可视化和大规模天气数据集建模，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。每个模块还将强调以下计算科学概念和实践：1)访问和操作数据的能力，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来构建定量的，科学的解释和预测大气系统中的事件和过程。教师将参与开发和测试新的教与学方法，以及开发一个框架，以支持教师正在努力创建整合计算思维科学实践的新教学材料。该项目将采用基于设计的研究方法来检验这样一个假设，即由大规模大气数据集支持并融合计算思维和实践的科学教育将：1)促进《下一代科学标准》所设想的有意义的科学学习，2)培养初高中学生的大气和计算科学素养。项目团队包括教学系统和劳动力发展、地球科学、电气和计算机工程方面的学术专家，以及一个高性能计算机合作实验室，该团队将与来自八个学区的教师合作，开发、测试和实施新的学习模块。该项目将直接涉及约44名中学教师和2000名学生。项目工作以五个目标为指导：1)开发和测试3D天气学习模块，通过数据分析和大规模天气数据的3D可视化和解释，将计算思维和实践融入大气科学学习；2)发展和开展教师专业发展，支持将计算思维和实践融入中学科学教学，并为中学科学学习提供大规模天气数据；3)研究将三维天气数据建模和可视化整合到地球与环境科学课程中如何支持学生和教师发展计算思维和基于模型的对大气系统复杂性的理解；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参与这些教学实践；5)制定一个基于研究的框架，指导教师未来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。

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.