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

我们正处于一场数据革命之中。无论是分析从复杂的制造过程中收集的传感器数据，还是从Facebook或亚马逊收集的客户偏好数据，还是通过Apple Watch或Fitbit等可穿戴技术传输的实时患者健康数据；今天的工程师经常被要求利用大型数据集来解决性质越来越复杂和抽象的问题。然而，本科工程师的培养方式几乎没有相应的变化，因此，许多人没有做好充分准备，在进入劳动力大军时自信地解决这些问题。此外，缺乏受过充分培训的研究人员来研究高等教育中的此类教学问题。这个项目的研究团队试图解决这两个问题，他们将一名经验丰富的工程教育研究员与一名经验较少的工程教育研究员配对，形成师徒关系。我们的目标是探索使用设计思维来培养工科学生解决数据丰富、包含不确定性和歧义元素的抽象问题的能力。虽然现有的大部分围绕工程设计的学术和实践都集中在物理人工制品的开发上，但我们认为，它的潜力在很大程度上还没有被开发出来，因为它应用在这个以数据为中心的新颖环境中。我们引入了一种教学方法，在概念课程中促进工程设计思维，以更好地为工程学学生加入当代STEM劳动力做好准备。通过案例研究方法，我们的具体目标是促进我们对如何利用工程设计来解决本科概率与统计课程中出现的模棱两可的、数据驱动的工程问题的理解，同时影响学生概念化、解决和交流概率与统计入门问题的解决方案的方法。指导这项研究的有两个研究问题。首先，“概率统计入门课程的内容、评估和教学方法可以通过什么方式进行修改，以促进本科工程专业学生的设计思维和对歧义的容忍度？”第二，“设计思维的发展能在多大程度上影响工科学生在处理数据驱动的工程问题时对歧义的容忍度？”拟议的案例研究包括三个阶段。在重新设计阶段，研究小组将批判性地检查现有的概率统计课程设计，并调整内容、评估和教学方法，以重新想象课程概念是如何以一种也包括强调设计思维的方式引入和评估的。然后，课程将围绕一个为期一学期的项目进行重新设计，这将要求学生团队：从开放式项目的选项中进行选择，利用所学的设计思维和课程概念来解决问题，并将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，并收集定性和定量的数据来解决研究问题。定性数据将包括岗位前的歧义容忍度评分。在这个最后阶段，将对数据进行分析，并将结果通过会议记录和期刊出版物传播给南佛罗里达大学校园的同事和更广泛的工程教育界。我们希望我们的发现不仅会影响重点机构教授工程课程的方式，还会带来可以被其他工程教育学者利用的见解。除了提供工程教育文献中的见解外，Ref项目中固有的导师-学员关系旨在扩大工程教育学者社区。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.