As engineering educators prepare increasingly diverse cohorts of students to tackle complex global challenges, the need for engineers with inclusive mindsets has become more apparent. One aspect of inclusion is the awareness of our potential for biases in the models we create of the world -- mathematical models that go on to influence the technologies we produce.
This paper presents a work-in-progress case study of an intervention in a middle-years analytical course with a heavy focus on mathematical modeling. The intervention is designed to make students aware of biases in mathematical models, their own tendencies towards these kinds of biases, and the sorts of impacts these biases can have on real populations. An important component of the intervention is that it is embedded into the teaching of analytical content, rather than being an additional unit on “inclusion” that remains separate from quantitative work.
I am a lecturer in the department of biomedical engineering at Georgia institute of technology /Emory University. I have been working on educational research since 2016. My main focus is on problem based learning core courses. But specifically I work on
Mel Chua (melchua.com) is a contagiously enthusiastic hacker, scholar, dancer, bimodal polyglot, and perpetual motion machine. She started out in electrical/computer engineering and spent several years wrangling large distributed technical teams in the open source software and hardware world before returning to academia. Mel's research focuses on learning in hacker/maker communities, faculty development, embodied and critical postmodern qualitative research methodologies, and prototyping alternate ontologies of curricular culture in engineering education.
Joe Le Doux is the Executive Director for Learning and Training in the Department of Biomedical Engineering at Georgia Tech and Emory University. Dr. Le Doux's research interests include narrative and inclusive pedagogies and practices.
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