Collaborative Research: Computational Modeling for Integrating Science and Engineering Design: Model Construction, Manipulation, and Exploration

科学与工程设计综合计算建模(CMISE)将进行一系列实验，系统地比较五、六年级学生在工程设计过程中的不同计算建模活动，他们对工程、科学和计算思维概念的理解，以及科学教师实施STEM和计算综合课程的信心和能力。计算建模涉及很高的认知负荷，到目前为止，研究还不清楚回报是主要涉及学习计算，还是学生的科学和工程学习也会受益。CMISE将调查不同类型的计算建模活动如何促进理工科学生的综合学习。CMISE将对将在该项目实施的大都会纳什维尔公立学区提供的STEM+计算产品产生直接影响；总的来说，它还将通过为五年级和六年级的学生带来真实而引人注目的科学和工程机会，帮助加强和发展多样化的STEM劳动力队伍。该项目还将为设计师和研究人员提供经验证据，说明如何在不同的环境下将计算机建模与科学和工程学习活动有效地结合起来。CMISE课程和教师支持材料将通过项目网站免费提供，使这些资源能够接触到研究范围以外的广泛教师。CMISE将利用先前开发和改进的符合下一代科学标准的课程单元，将城市径流的地球科学概念与一个有意义的工程设计问题结合起来，供五、六年级的学生进行基础研究，以更好地了解不同类型的计算建模活动如何协调科学和工程学习之间的联系。CMISE将进行一系列设计实验，系统地比较三种计算建模活动对学生工程设计过程的启示、他们对工程、科学和计算思维(CT)概念和实践的理解，以及科学教师实施STEM和计算综合课程的信心和能力。被比较的三项活动是计算模型(CM)构建(学生用给定的编程语言对科学现象进行建模)、CM操作(学生检查代码或针对给定的CM对科学现象的模拟)和CM探索(学生在不查看底层代码的情况下探索对科学现象的给定模拟)。CMISE采用了强大的理论框架和系统的设计实验方法，以促进学生如何使用不同类型的计算建模活动进行交互和学习的学习理论。它将运用定量和定性分析的方法，将已有的统计分析方法与新的分析方法相结合，得出学生在三种实验条件下的学习行为与表现之间的关系。这项设计实验研究将解开这些条件如何影响科学、工程和计算机的协同学习。该项目由EHR核心研究(ECR)和CS for All：Research和RPPS计划共同资助。ECR支持推进STEM学习和学习环境的基础研究、扩大对STEM的参与以及STEM劳动力发展的工作。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。

Computational Modeling for Integrating Science and Engineering Design (CMISE) will conduct a series of experiments to systematically compare different computational modeling activities on 5th and 6th grade students’ engineering design processes, their understanding of engineering, science and computational thinking concepts, as well as science teachers’ confidence and ability to implement integrated STEM and computing curricula. Computational modeling involves a high cognitive load, and research to date is unclear whether the payoff primarily entails learning computing or whether students’ science and engineering learning benefit as well. CMISE will investigate how different types of computational modeling activities promote integrated student learning of science and engineering. CMISE will have immediate impacts on STEM + Computing offerings for the Metro Nashville Public School district where the project will be conducted; broadly it will also help strengthen and grow a diverse STEM workforce by bringing authentic and compelling science and engineering opportunities to fifth and sixth grade students. This project will also provide designers and researchers with empirical evidence for how to effectively integrate computer modeling with science and engineering learning activities in different settings. The CMISE curriculum and teacher support materials will be made freely available through project website, allowing these resources to reach a wide range of teachers beyond those included in the study. CMISE will leverage a previously developed and refined Next Generation Science Standards-aligned curriculum unit that integrates the Earth Science concept of urban water runoff with a meaningful engineering design problem for fifth- and sixth grade students to conduct fundamental research to better understand how different types of computational modeling activities mediate the connections between science and engineering learning. CMISE will conduct a series of design experiments to systematically compare the affordances of three computational modeling activities on students’ engineering design processes, their understanding of engineering, science and computational thinking (CT) concepts and practices, as well as science teachers’ confidence and ability to implement integrated STEM and computing curricula. The three activities being compared are computational model (CM) construction (where students model a science phenomenon in a given programming language), CM manipulation (where students inspect either the code or the simulation for a given CM of a science phenomenon), and CM exploration (where students explore a given simulation of a science phenomenon without viewing the underlying code). CMISE adopts a strong theoretical framing and a systematic design experiment approach to contribute to the learning theory of how students interact with and learn using different types of computational modeling activities. It will apply quantitative and qualitative analysis methods to combine established statistical analysis methods with novel analytics approaches and derive relations between students' learning behaviors and performance in the three experimental conditions. This design experiment studies will disentangle how these conditions influence the synergistic learning of science, engineering, and CT.This project is co-funded by the EHR Core Research (ECR) and CS for All: Research and RPPs programs. ECR supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.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.