Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
Diversity and NSF Grantees Poster Session
12
10.18260/1-2--43089
https://peer.asee.org/43089
311
Dr. Jeremy A. Magruder Waisome is the Thomas O. Hunter Rising Star Assistant Professor in the Engineering Education Department at the University of Florida (UF). Her research focuses on self-efficacy and critical mentoring in the context of engineering and computer science education. She is passionate about broadening participation in engineering, leveraging evidence-based approaches to improve the engineering education environment.
Dennis Parnell Jr. is a Ph.D. student in the Department of Engineering Education at the University of Florida. His research focuses on understanding and improving underrepresented student retention and persistence in engineering. For his doctoral research, Dennis is leveraging emerging learning technologies to broaden participation in engineering by exposing students to semiconductor fabrication processes. Much of his work involves designing and assessing interventions for extra- and co-curricular activities for students throughout the educational ecosystem. He is also a member of the ASEE CDEI Spotlight Team. Dennis holds a B.S. in mechanical engineering from The University of Alabama and a M.S. in mechanical engineering from the University of Florida.
Pavlo "Pasha" Antonenko is an Associate Professor of Educational Technology at the University of Florida. His interests focus on the design of technology-enhanced learning environments and rigorous mixed-method research.
The effective introduction of the fundamentals of artificial intelligence (AI) to middle school students requires the novel integration of the existing science curriculum and AI concepts. This research focuses on leveraging 6th and 7th-grade science curricula related to state standards to introduce machine learning concepts by using fossil shark teeth. Researchers from engineering, education, and paleontology collaboratively developed learning modules to upskill Title I schoolteachers to meaningfully integrate AI fundamentals within their existing curriculum. With a special emphasis on machine learning (ML), five lesson plans were presented during a week-long teacher professional development. Teachers conceptualized and implemented ML models that distinguish fossil shark teeth by their taxonomy and primary functions to recognize ecological and evolutionary patterns. After introducing a lesson, each teacher curated the lesson plan content to directly relate to their specific context, in collaboration with each other and our research team.
We built the curriculum leveraging students’ existing conceptions and misconceptions about AI from prior work while testing the feasibility of addressing AI learning objectives, as well the AI4K12’s Five Big Ideas, in the broader context of middle school science, technology, engineering, mathematics, and computing (STEM+C) education. Our lessons were scaffolded using the machine learning development process: 1) data collection and preparation; 2) selecting and training the model; 3) evaluating the models’ accuracy; 4) tuning model parameters to improve performance. Each stage of the development process constituted a different lesson during a week-long summer professional development. Through these lessons, teachers were introduced to several open-source AI tools, including two platforms used to build/train ML models: Google’s Teachable Machine and Roboflow. The fifth and final day of the professional development gave teachers time to conceptualize how these lessons could be integrated with their existing curricula.
Initial feedback from the summer PD indicated we overestimated the teachers’ familiarity with technology. More time was necessary to orient teachers to each AI tool. Teachers readily adopted the use of Seek by iNaturalist and myFossil. However, the teachers’ use of AI tools in their classrooms highly favored Google’s Teachable Machine to Roboflow, which may relate to the affordances and constraints of each tool. Preliminary mixed-method data analyses show teachers' self-efficacy around teaching AI improved after engaging in the summer PD. Longitudinal data collection is underway and will inform future work related to improving teacher and student self-efficacy related to teaching and learning AI, respectively.
Waisome, J. A. M., & Parnell, D. R., & Antonenko, P., & Abramowitz, B., & Perez, V. (2023, June), Board 385: Shark AI: Teaching Middle School Students AI Fundamentals Using Fossil Shark Teeth Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43089
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