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，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日益复杂和抽象的问题。然而，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市场时没有做好充分的准备来自信地解决这些问题。此外，缺乏受过适当培训的研究人员来研究高等教育中的这类教学问题。该项目的研究团队，旨在解决这两个问题，配对经验丰富的工程教育研究人员与经验较少的工程教育研究人员在导师-学员关系。我们的目标是探索使用设计思维来训练工程专业的学生解决数据丰富的抽象问题，包括不确定性和模糊性的元素。虽然大部分现有的学术和实践围绕工程设计是围绕着一个物理工件的发展，我们认为，它的潜力在很大程度上尚未开发，应用在这个新颖的，以数据为中心的context.We引入教学方法，以促进工程设计思维的概念课程，更好地准备工程专业的学生加入当代STEM劳动力。使用案例研究的方法，我们的具体目标是提高我们的理解，如何利用工程设计来解决模糊的，数据驱动的工程问题，在本科概率和统计课程，同时影响学生的方法来概念化，解决和沟通的解决方案，介绍概率和统计问题。有两个研究问题指导这项研究。首先，“在什么样的方式可能的内容，评估和教学方法的介绍概率和统计课程进行修改，以促进设计思维和宽容的模糊性本科工程专业的学生？”第二，“设计思维的发展在多大程度上可以影响工程专业学生在处理数据驱动的工程问题时对模糊性的容忍度？”拟议的案例研究包括三个阶段。在重新设计阶段，研究团队将严格审查现有的概率和统计课程设计，并调整内容，评估和教学方法，以重新想象课程概念是如何引入和评估的方式，也包括强调设计思维。然后，课程将围绕一个学期的项目进行重新设计，该项目要求学生团队：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，并收集定性和定量数据来解决研究问题。定性数据将包括模糊容忍量表的前后评分。在最后阶段，将对数据进行分析，结果将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。我们希望我们的研究结果不仅会影响工程课程在重点机构的教学方式，而且会导致其他工程教育学者可以利用的见解。除了在工程教育文献中产生见解外，该RIEF项目中固有的导师-学员关系旨在扩大工程教育学者的社区。该奖项反映了NSF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。

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.