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

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.