Our Research

The nature of engineering is messier than engineering education would lead students to believe.

Practicing engineers address problems that are open-ended (they have no single correct answer), ill-defined (they have conflicting constraints and outcome metrics), and sociotechnical (they have good and bad impacts on society). However, problems in engineering science courses—the non-design courses that students take in their sophomore and junior years—are often closed-ended (they have a single correct answer), well-defined (the system has already been simplified to conform to the assumptions of a mathematical model), and lack connection to the real world. These “textbook” problems are certainly important for practicing mathematical problem-solving processes, but they lack the complexity of real-world engineering problems. While we cannot expect undergraduate students to tackle complex engineering problems in the same way as an experienced engineer, we work to create opportunities for students to engage in the productive beginnings of professional engineering practices, while developing a generalizable understanding of these productive beginnings. In this way, our research features a close and symbiotic relationship between research and practice. Developing new learning activities provides an opportunity to study professional practices, and the results of this research enable us to improve the learning activities.

Our research focuses broadly on two topics: understanding 1) how students engage in the productive beginnings of professional practices and 2) how instructors’ pedagogy and assignments can support these productive beginnings.

For a list of our publications on these projects, please see this page.

Engineering Judgment

The first element of professional practice we study is engineering judgment, which is the use of mathematical models in design and analysis. The core engineering practice in creating a real-world system is not the calculation of the answer—as in a textbook problem—but the modeling of the problem. Before applying the canonical mathematical models learned in their undergraduate education to these ill-defined problems, engineers must first decide what assumptions can and cannot be made in order to select an accurate and efficient model.

Our lab, along with our collaborator Prof. Jessica Swenson, are building on Julie Gainsburg’s framework of engineering judgment to develop a new theoretical framework of the productive beginnings of engineering judgment. This research is conducted in a new type of assignment we developed called Open-Ended Modeling Problems (OEMPs). OEMPs ask students to make and justify their own assumptions to apply canonical mathematical models to a real-world system. You can see an example OEMP that was used in Aero 215 at Michigan here: Part 1 and Part 2. Our research team now includes faculty from six universities, all of whom have designed and implemented their own OEMPs.

Leading an OEMP discussion in front of the Nomad aircraft.

Our future research includes collecting and qualitatively analyzing students’ verbal discourse to understand, explain, argue, and develop assumptions and models as they work on OEMPs together. Our ultimate goal is to develop an understanding of the progression of engineering judgment from undergraduate to experienced engineer, starting in Fall 2021 with a project how students use their engineering judgment during open-ended co-curricular activities such as internships and student project teams.

You can also hear Aaron talk about this project in the U-M EER Seminar he gave in October, 2021:


The second element of professional practice that we study is macroethical reasoning, the ability to consider the broader societal context and implications of engineered systems. When choosing a career or designing a system, engineers must consider the societal impacts of their work with respect to their own personal code of ethics. For this reason, students must engage in the productive beginnings of macroethical reasoning throughout their undergraduate education. However, engineering science courses rarely focus on macroethics; at best, students might briefly consider the societal implications of technology during a design course.

At CU Boulder Aaron led a project in which a group of awesome students helped to develop a macroethics modules for the Spring 2021 Vehicle Design and Performance course. In these modules, students had a structured discussion on an issue—such as space settlement—after reading information from various perspectives.

In our future research with Prof. Corey Bowen, we are interested in creating more ways to include macroethics content in engineering science courses, particular in aerospace engineering (Aaron’s home department). We also want to understand how students understand, consider, discuss, and make decisions about macroethical issues in engineering. We’re also interested in the longer-term effects of macroethics lessons, such as students’ abilities to have respectful, productive conversations outside the classroom about these issues in engineering and the development of critical consciousness, in which students come to understand the way in which engineering contributes to systemic oppression.

Responsive Teaching in Engineering

Our third research interest focuses on formative assessment when instructors are engaging students in these productive beginnings of professional practices. We study formative assessment in the context of responsive teaching—how instructors elicit, notice, interpret, and respond to the disciplinary substance of student thinking. Responsive teaching has been found to increase student conceptual understanding and provide an opportunity for students to engage in professional practices. However, responsive teaching has not been the focus of much research in engineering education, particularly in large, lecture-based engineering science courses. We’re interested in factors that facilitate responsive teaching, the impact that responsive teaching has on students’ learning, and online tools for supporting formative assessment during open-ended, ill-defined assignments.