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对人、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成果促进人类和环境的福祉，我们需要首先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。为了实现这一理解，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的理论与工程设计实践联系起来。特别是，该项目将检查学生在全国各地的土木和化学工程项目的信念和做法，对国家的基础设施和环境有重大影响的两个领域。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。这种预测反过来将有助于教育工作者更有效地培养学生的能力，以说明他们的工作的更广泛的影响。在这样做的过程中，这个项目也将帮助教育工作者更好地了解学生如何将他们在本科时对设计的学习转移到工作场所的设计实践中。这种转变尚未得到很好的理解，但对于工程师根据对社会和环境的长期影响来塑造现实世界的决策至关重要。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本科课程和课程中的位置，教育工作者可以帮助学生更好地认识和理解他们的工作的影响，并利用这种理解做出更有可能导致积极的未来影响的决定。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，以提高教育研究在处理大规模定性数据集和新的信息格式的能力。本项目将计划行为理论与心理模型相结合，构建了关于（1）社会生态系统（SES）的工程师心理模型、（2）学生心理模型随时间的变化、（3）心理模型与工程学校和工程工作中的设计决策之间的关系等新的基础知识。通过利用和扩展自然语言处理和贝叶斯统计学的最新进展，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，但目前在该领域未得到充分利用。来自全国各地大学的化学（n=250）和土木（n=250）顶级课程的高级工程专业学生将被招募并随后进入毕业后就业。选择这些学科是因为它们对国家基础设施和环境的潜在影响，因为先前的工作已经确定了这些工程子学科之间的心理模型的潜在差异，突出了比较研究的必要性。 工作期间的顶点是研究的中心焦点，因为顶点提供了一个理想的环境来检查SES和设计决策的心理模型之间的关系，但这些关系可能会改变毕业生遇到竞争的利益，行业规范，并在工程实践中增加复杂性。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心理模型及其设计决策。识别和理解这些系统的心理模型如何为参与者从正规教育过渡到劳动力的设计决策提供信息，可以为工程师队伍的发展提供信息，并更好地为工程师设计可持续性和应对新兴的社会技术和环境挑战做好准备。ECR计划强调基础STEM教育研究，产生该领域的基础知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。