2025 ASEE Annual Conference & Exposition

In this full methods paper, we discuss data generation methods and considerations for eliciting and engaging belief sets in engineering education research. Belief sets are interrelated in complex ways to our other beliefs, our behavior, and cultural norms. Further, researchers have established that engineers and engineering students hold beliefs that function as barriers to equity in engineering education and practice. Therefore, beliefs continue to be a key mechanism through which change can occur. Despite their pervasiveness, belief sets are difficult to engage consciously given their complexity, contradictory nature, nested-ness and varying levels of awareness. Since there are significant limitations to eliciting beliefs with traditional (e.g., survey, interview) methods in engineering education, there is a need to develop new methods to enable engineering education researchers to elicit beliefs beyond what engineering students directly espouse.

We are a research team working on an NSF-funded project exploring belief-eliciting methods to enable reflexivity in engineering service-learning students. Presently, we have completed one year of the project during which we have focused on developing methods to this end. Our experience as a research team is what informs this paper.

The objective of this paper is to advance data generation methods in engineering education that enable researchers and participants to elicit aspects of their complex belief sets. Three different data generation methods were adapted for engineering education beliefs research: vignettes, photo elicitation, and concept mapping based in sociology, anthropology, and psychology, respectively. We will briefly review the development and disciplinary context of each method, previous uses in engineering education, and the adaptation of the method for the purpose of eliciting aspects of complex belief sets. While the context of this research is engineering service-learning, we anticipate that our methodological findings can be translated to elicit complex belief sets in other research contexts.