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

计算建模整合科学和工程设计（CMISE）将进行一系列的实验，系统地比较不同的计算建模活动对五年级和六年级学生的工程设计过程，他们对工程，科学和计算思维概念的理解，以及科学教师实施综合STEM和计算课程的信心和能力。计算建模涉及高认知负荷，迄今为止的研究还不清楚回报主要是学习计算还是学生的科学和工程学习也受益。CMISE将研究不同类型的计算建模活动如何促进学生对科学和工程的综合学习。CMISE将对该项目将进行的Metro纳什维尔公立学校区的STEM +计算产品产生直接影响;广泛地说，它还将通过为五年级和六年级学生带来真实和引人注目的科学和工程机会，帮助加强和发展多元化的STEM劳动力。该项目还将为设计人员和研究人员提供经验证据，说明如何有效地将计算机建模与不同环境中的科学和工程学习活动相结合。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广泛的教师，而不仅仅是研究中的教师。 CMISE将利用先前开发和完善的下一代科学标准对齐课程单元，将城市水径流的地球科学概念与五年级和六年级学生有意义的工程设计问题相结合，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调解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。比较的三个活动是计算模型（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（学生检查代码或科学现象的给定CM的模拟）和CM探索（学生在不查看底层代码的情况下探索科学现象的给定模拟）。CMISE采用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。将运用定量和定性分析方法，将联合收割机已建立的统计分析方法与新的分析方法相结合，得出三种实验条件下学生学习行为与成绩之间的关系。这个设计实验研究将解开这些条件如何影响科学，工程和CT的协同学习。该项目由EHR核心研究（ECR）和CS共同资助：研究和RPPs计划。ECR支持推进STEM学习和学习环境的基础研究，扩大STEM参与和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。

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.