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

可持续性设计：社会生态系统的心理模型如何影响工程设计决策

• 批准号: 2300977
• 负责人: Andrew Katz
• 金额: $ 82.64万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300977&HistoricalAwards=false
• 关键词: Design; Sustainability; How; Mental; Models

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.

工程师所做的工作和他们创造的设计会对人类、基础设施和环境产生长期的、往往是不可预见的影响。为了帮助确保工程工作的结果促进人类和环境的福祉，我们首先需要了解如何影响。工程师目前将他们的技术工作与这些更广泛的背景和系统联系起来，为了实现这种理解，该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。 ，该项目将审查学生在公民社会中的信仰和实践和化学项目工程，这两个领域对国家的基础设施和环境产生重大影响，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的社会和生态系统心理模型将如何影响他们的工程设计工作。反过来，该项目还将帮助教育工作者更有效地培养学生的能力，以解释其工作的更广泛影响。在此过程中，该项目还将帮助教育工作者更好地了解学生如何将本科生的设计知识转化为设计实践。这种转变尚未得到充分理解，但鉴于对社会和环境的长期影响，对于制定工程师的现实决策至关重要。这种对正规教育和专业实践之间联系的关注将有助于确定教育工作者可以在本科课程和项目中为学生提供帮助的地方。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带来积极未来影响的决策。最后，该项目使用自然语言处理和贝叶斯统计等先进的分析方法来提高教育研究的能力。处理大规模定性数据集和新颖的信息格式。该项目将计划行为理论与心智模型相结合，以建立关于以下方面的新基础知识：(1) 社会生态系统 (SES) 的工程师心智模型，(2) 学生心智模型随时间的变化，以及 (3) 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，但目前尚未实现。化学领域的高级工程学生未得到充分利用。将从全国范围内的一系列大学招募顶尖课程（n = 250）和土木（n = 250），并在毕业后就业。选择这些学科既是因为它们对国家基础设施和环境的潜在影响，也是因为它们对国家基础设施和环境的潜在影响。因为之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。顶点工作阶段是研究的中心焦点，因为顶点提供了一个理想的环境来检查 SES 和心理模型之间的关系。设计决策，但那些随着毕业生遇到竞争利益、行业规范和工程实践复杂性的增加，关系可能会发生变化，将在多个时间点收集定量和定性数据的组合，以衡量参与者的 SES 心理模型以及他们的设计决策。了解当参与者从正规教育过渡到劳动力市场时，这些系统的心智模型如何为设计决策提供信息，可以为工程人员的发展提供信息，并更好地帮助工程师进行可持续性设计并应对新出现的社会技术和环境挑战。该项目得到了 NSF 的 EDU 核心研究（ ECR）程序。强调在该领域产生基础知识的基础 STEM 教育研究：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 NSF 的法定使命。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。