The traditional engineering design process taught in universities across the country focuses on several common design steps. Often these experiences place little emphasis on creating value by defining a market opportunity or communicating the overall economic and social impact. In collaboration with KEEN, a network of thousands of engineering faculty working to unleash undergraduate engineers so that they can create personal, economic, and societal value through the entrepreneurial mindset, a large mid-western university is adding multiple entrepreneurial minded learning (EML) elements to an existing first-year course. This Work-in-Progress paper represents the first phase of a four-phase, 18-month pilot, during which we explored the impact of EML in first-year engineering classrooms on motivation and identity.
This phase used a mixed methods investigation into the current practices of five KEEN related first-year engineering programs currently incorporating EML elements into their curricula. Researchers visited each school or program and collected data via focus groups with first-year engineering faculty who implement EML in the classroom, surveys of first- and fourth-year students to assess the short- and long-term impacts of EML at it relates to motivation and identity, and observations of EML classrooms to note current engagement in courses with EML practices.
We mapped the findings from the information collected to the KEEN engineering mindset and skillsets along with the Longitudinal Model of Motivation and Identity (LMMI), which combines self-determination theory (SDT) needs (autonomy, competence, and relatedness) with possible-selves theory (PST). The LMMI served as a lens for considering the motivational and identity impacts that EML experiences have on engineering students’ motivation and identity. Our analysis included deductive coding of the focus groups followed by open coding to break down the items to better understand exactly what is contributing to student motivation and identity. We triangulated these findings with our observations and student survey data to identify common trends. Additionally, we used descriptive statistics to analyze the survey data. As this is a mixed methods study, we also employed mixing to find connections between all of our data sets.
Once mapping is complete, the results from this phase will be used to develop a set of best practices that will be incorporated into EML projects, courses, and curriculum during future phases to encourage autonomous motivation and identity development. A significant contribution of our project is the operationalization of LMMI in the context of EML along with the future curriculum that will be developed out of our work.
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