Episode 13: Improving Mathematics Accessibility with Ann Gulley Artwork

Engineering Change Podcast

Engineering Change is the podcast designed to help REDEFINE engineering as we know it by: RE-imaging who we see as engineers and what we see as engineering, DE-siloing academic programs and problem solving, and FINE-tuning culture and climate using best practices for justice, equity, diversity, and inclusion (JEDI) so people from all backgrounds and identities can succeed. It's about being just as intentional with JEDI as we are with solving any other problems in engineering; applying a carefully planned, iterative process that includes the stakeholders from problematization through ideation, evaluation and ultimately, selecting the best solutions. Each episode leaves the audience with strategies you can put into practice wherever you are in the process of ENGINEERING CHANGE.

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Engineering Change Podcast

Episode 13: Improving Mathematics Accessibility with Ann Gulley

December 02, 2020 • Dr. Yvette E. Pearson • Season 2 • Episode 13

People with disabilities are often left out of conversations and initiatives that support diversity, equity, and inclusion. With a sizeable fraction of the US population having some type of disability, it is unfathomable and unfortunate that people often count us out when it comes to degrees and careers in engineering and other STEM fields. There is an urgent need to RE-IMAGE who we see as engineers and FINE-TUNE our climate and culture, eradicating barriers to success for all students.

In this episode, I speak with Ann Gulley, Tutor Coordinator in the Ginn College of Engineering at Auburn University, about Process-Driven Math - her team's novel method of teaching and assessing mathematics in a way that reduces barriers for students with and without disabilities. We also spend a bit of time talking about very practical aspects of education such as how standards are (or should be) set and creating tutoring programs that are supportive of and that equitably engage students from all backgrounds and identities.

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Support for some of the work described in this episode was provided by the National Science Foundation under award numbers 1726869 and 1726254. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the researchers and do not necessarily reflect the views of the National Science Foundation.