Design for Sustainability: How Mental Models of Social-Ecological Systems Shape Engineering Design Decisions

工程师所做的工作和他们所创造的设计对人、基础设施和环境有着长期的、通常是不可预见的影响。为了确保工程工作的成果能够促进人类和环境的福祉，我们首先需要了解工程师目前如何将他们的技术工作与这些更广泛的背景和系统联系起来。为了达到这种理解，本项目将整合认知和行为科学、社会理论和先进的计算方法，将心理模型和计划行为理论与工程设计实践联系起来。特别是，该项目将检查全国土木和化学工程专业学生的信仰和实践，这两个领域对国家的基础设施和环境有重大影响。研究结果将使未来的研究人员和教育工作者能够预测学生对社会和生态系统的心理模型将如何影响他们的工程设计工作。这样的预测反过来将帮助教育工作者更有效地培养学生解释其工作的更广泛影响的能力。这样做，这个项目也将帮助教育工作者更好地理解学生如何将他们在本科学习的设计知识转化为工作场所的设计实践。这种转变还没有被很好地理解，但鉴于对社会和环境的长期影响，在塑造工程师的现实世界决策方面，这是至关重要的。这种对正规教育和专业实践之间联系的关注将有助于在本科课程和项目中确定教育者可以帮助学生更好地认识和理解他们工作的影响，并利用这种理解做出更有可能导致积极未来影响的决定。最后，该项目使用先进的分析方法，如自然语言处理和贝叶斯统计，以提高教育研究处理大规模定性数据集和新信息格式的能力。该项目将计划行为理论与心理模型相结合，旨在建立以下方面的新基础知识：(1)工程师对社会生态系统（SES）的心理模型；(2)学生心理模型随时间的变化；(3)在工程学校和工程工作中心理模型与设计决策之间的关系。通过利用和扩展自然语言处理和贝叶斯统计的最新进展，该项目将测试和验证一种强大的定性数据分析方法，该方法允许更大的样本量，但目前在该领域尚未得到充分利用。将从全国多所大学招收攻读顶点课程的化学专业（n=250）和土木专业（n=250）的高级工程专业学生，并跟踪他们毕业后就业。选择这些学科，一方面是因为它们对国家基础设施和环境的潜在影响，另一方面是因为先前的工作已经确定了这些工程子学科之间心理模型的潜在差异，这突出了比较研究的必要性。顶点到工作阶段是本研究的中心焦点，因为顶点阶段提供了一个理想的环境来检验SES心理模型和设计决策之间的关系，但是这些关系可能会随着毕业生遇到竞争利益、行业规范和工程实践中日益复杂的情况而改变。将在多个时间点收集定量和定性相结合的数据，以测量参与者的SES心理模型及其设计决策。识别和理解这些系统的心智模型如何在参与者从正规教育过渡到劳动力的过程中为设计决策提供信息，可以为工程劳动力的发展提供信息，并使工程师更好地为可持续性设计做好准备，并应对新兴的社会技术和环境挑战。本项目由美国国家科学基金会EDU核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。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。

The work engineers do and the designs they create have long-term, often unforeseen, impacts on people, infrastructure, and the environment. To help ensure that the results of engineering work advance human and environmental well-being, we need to first understand how engineers currently connect their technical work to these broader contexts and systems. To achieve this understanding, this project will integrate cognitive and behavioral science, social theory, and advanced computational methods to connect theories of mental models and planned behavior with engineering design practice. In particular, the project will examine the beliefs and practices of students in civil and chemical engineering programs across the country, two fields that have substantial impacts on the Nation’s infrastructure and environment. The results will enable future researchers and educators to predict how students’ mental models of social and ecological systems will inform their engineering design work. Such predictions in turn will assist educators to more effectively develop students’ ability to account for the broader impacts of their work. In doing so, this project will also help educators better understand how students transfer their learning about design as undergraduates into the practice of design in the workplace. This transition is not yet well-understood but is critically important in shaping engineers’ real-world decisions in light of long-term impacts on society and the environment. This focus on the links between formal education and professional practice will help to identify places in undergraduate courses and programs where educators can assist students to better recognize and understand the impacts of their work and use that understanding to make decisions more likely to lead to positive future impacts. Finally, the project uses advanced analytic approaches such as natural language processing and Bayesian statistics to increase the capacity of educational research in dealing with large-scale qualitative data sets and novel information formats. The project integrates the theory of planned behavior with mental models to build new fundamental knowledge about (1) engineers’ mental models of social-ecological systems (SES), (2) changes in students’ mental models over time, and (3) relationships between mental models and design decisions in both engineering school and engineering work. By leveraging and extending recent advances in natural language processing and Bayesian statistics, the project will test and validate a powerful approach to qualitative data analysis that allows for much larger sample sizes but is currently underused in the field. Senior engineering students in chemical (n=250) and civil (n=250) in capstone courses from a range of universities across the country will be recruited and followed into post-graduation employment. These disciplines are chosen both because of their potential impact on national infrastructure and environments and because prior work has identified potential differences in mental models among these engineering subdisciplines that highlight the need for comparative research. The capstone to work period is the central focus in the study because capstone provides an ideal environment to examine the relationships between mental models of SES and design decisions, but those relationships may change as graduates encounter competing interests, industry norms, and increased complexity in engineering practice. A combination of quantitative and qualitative data will be collected at multiple time points to measure participants’ mental models of SES as well as their design decisions. Identifying and understanding how mental models of these systems inform design decisions as participants transition from formal education into the workforce can inform engineering workforce development and better prepare engineers to design for sustainability and meet emerging sociotechnical and environmental challenges.This project is supported by NSF’s EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM 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.