Collaborative Research: Research Initiation: Leveraging Design Thinking to Deal with Ambiguity Embedded in Data-Driven Engineering Problems

协作研究：研究启动：利用设计思维处理数据驱动工程问题中的模糊性

• 批准号: 2106242
• 负责人: Kingsley Reeves
• 金额: $ 12.52万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106242&HistoricalAwards=false
• 关键词: Collaborative; Research; Initiation; Leveraging; Design

We are in the midst of a data revolution. Whether it is analyzing sensor data collected from a complex manufacturing process, customer preference data harvested from Facebook or Amazon, or real-time patient health data transmitted via wearable technology like an Apple Watch or Fitbit; today’s engineers are routinely asked to leverage large data sets to solve problems that are increasingly complex and abstract in nature. However, there has been little corresponding change in the way that undergraduate engineers are trained and, as a result, many are not adequately prepared to confidently address such problems when they enter the workforce. Moreover, there is a dearth of researchers adequately trained to study such pedagogical problems in higher education. The research team for this project, which seeks to address both issues, pairs a seasoned engineering education researcher with a less experienced engineering education researcher in a mentor-mentee relationship. We aim to explore the use of design thinking to train engineering students to solve abstract problems that are data-rich and include elements of uncertainty and ambiguity. While much of the existing scholarship and practice surrounding engineering design is centered around the development of a physical artifact, we argue that its potential has been largely untapped as applied in this novel, data-centered context.We introduce a pedagogical approach to promote engineering design thinking in conceptual courses to better prepare engineering students to join a contemporary STEM workforce. Using a case study approach, our specific aim is to advance our understanding of how engineering design can be leveraged to solve ambiguous, data-driven engineering problems presented in an undergraduate probability and statistics course while influencing students’ approach to conceptualizing, solving, and communicating solutions to introductory probability and statistics problems. There are two research questions guiding this study. First, “In what ways might the content, assessment, and pedagogy of an introductory probability and statistics course be modified to facilitate design thinking and tolerance for ambiguity among undergraduate engineering students?” Second, “To what extent can the development of design thinking influence engineering students’ tolerance for ambiguity when dealing with data-driven engineering problems?” The proposed case study includes three phases. During the redesign phase, the research team will critically examine an existing probability and statistics course design and adapt the content, assessment, and pedagogy to reimagine how the course concepts are introduced and evaluated in a way that also includes an emphasis on design thinking. Then, the course will be redesigned around a semester-long project that will require student teams to: select among options for an open-ended project, leverage design thinking and course concepts learned to address the problem, and communicate their results to stakeholders. During the implementation phase, the research team will implement the pedagogical innovation, and collect qualitative and quantitative data to address the research questions. Qualitative data will include pre-post scores on the Tolerance for Ambiguity Scale. During this final phase, the data will be analyzed and the results will be disseminated to colleagues on the University of South Florida campus and to the broader engineering education community via conference proceedings and a journal publication. We expect our findings to not only impact how engineering courses are taught at the focal institution, but also lead to insights that can be leveraged by other engineering education scholars. In addition to producing insights situated in the engineering education literature, the mentor-mentee relationship inherent in this RIEF project is designed to extend the community of engineering education scholars.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.

我们正处于数据革命之中。无论是通过复杂的制造过程收集的传感器数据，从Facebook或Amazon收获的客户偏好数据，还是通过Apple Watch或Fitbit（例如Apple Watch或Fitbit）传输的实时患者健康数据；通常要求当今的工程师利用大型数据集来解决越来越复杂和抽象的问题。但是，对本科工程师的培训方式几乎没有相应的变化，因此，许多人在进入劳动力时并没有充分准备好的自信地解决此类问题。此外，经过足够培训的研究人员死亡，以研究高等教育中的这种教学问题。该项目试图解决这两个问题的研究团队，将经验丰富的工程教育研究人员与经验丰富的工程教育研究员配对，以心理损害的关系。我们旨在探索设计思维的使用来培训工程专业的学生，​​以解决数据丰富的抽象问题，并包括不确定性和歧义的元素。尽管围绕工程设计的许多现有科学和实践都是围绕物理工具的发展，但我们认为，它的潜力在这款以数据为中心的小说中所应用的很大程度上未被限制，我们引入了一种教学方法，以促进工程设计思维，以在概念课程中促进工程学课程，以更好地为工程学学生提供现代化的STEMPORESS学生。使用案例研究方法，我们的具体目的是促进我们对如何利用工程设计的理解来解决本科概率和统计课程中提出的模棱两可的，数据驱动的工程问题，同时影响学生在概念化，解决方案和传达解决方案以引入介绍性和统计问题的方法。指导这项研究有两个研究问题。首先，“简介概率和统计课程的内容，评估和教学法可以通过什么方式进行修改，以促进设计思维和本科工程专业学生歧义的宽容性？”其次，“在处理数据驱动的工程问题时，设计思维的发展在多大程度上会影响工程专业的学生对歧义的容忍度？”拟议的案例研究包括三个阶段。在重新设计阶段，研究团队将批判性地检查现有的概率和统计课程设计，并适应内容，评估和教学法，以重新想象课程概念的介绍和评估，以还包括对设计思维的重点。然后，该课程将在一个学期长的项目中重新设计，该项目将要求学生团队：在开放式项目的选项中进行选择，利用设计思维和课程概念学会了解决问题，并将其结果传达给利益相关者。在实施阶段，研究团队将实施教学创新，并收集定性和定量数据以解决研究问题。定性数据将包括对歧义量表的公差的预分数。在最后阶段，将分析数据，结果将通过会议记录和期刊出版物通过会议记录和杂志，将结果传播给南佛罗里达大学校园的同事和更广泛的工程教育社区。我们希望我们的发现不仅会影响焦点机构如何教授工程课程，还可以带来其他工程教育学者可以利用的见解。除了产生工程教育文献中的见解外，此RIEEF项目中固有的心理关系旨在扩展工程教育学者社区。这项奖项反映了NSF的法定任务，并通过使用基金会的知识分子和广泛的影响审查标准通过评估来诚实地对其进行评估。