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Exploring K-12 Teachers’ Confidence in Using Machine Learning Emerging Technologies through Co-design Workshop (RTP)

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Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

Mr. Burns' Brainchild: AI in the Springfield STEM Classroom, Release the Hounds!

Tagged Division

Pre-College Engineering Education Division (PCEE)

Permanent URL

https://strategy.asee.org/47419

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Paper Authors

biography

Geling Xu Tufts Center for Engineering Education and Outreach

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Geling (Jazz) Xu is a Ph.D. student in STEM Education at Tufts University and a research assistant at Tufts Center for Engineering Education and Outreach. She is interested in K-12 STEM education, makerspace, how kids use technology to solve real-world problem, AI education, robotics education, playful learning, and course design.

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biography

Milan Dahal Tufts Center for Engineering Education and Outreach

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I am a graduate student in Mechanical Engineering at Tufts University. After completing my undergraduate degree in Electronics and Communications Engineering in Nepal I was selected as a Teach for Nepal fellow. My experience of working as a public school teacher for three years inspired me to work on increasing access to education for underprivileged students. I am motivated to finding solutions to bridge the gaps of inequity in education through design and technology.

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Brian Gravel Tufts University

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Dr. Gravel is an assistant professor of education and the Director of Elementary STEM Education at Tufts University.

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Abstract

Artificial Intelligence(AI) and Machine Learning (ML) touch every aspect of modern life and will continue to influence us more than ever in the future. We think schools and teachers should be prepared to let the children explore ML to help them understand how the world around them functions. It has been shown that children as young as three years old can not only interact with ML technologies but also produce ML data sets and models[1].

In this paper, we explore factors influencing the growth of teacher confidence in the implementation of emerging ML technologies within engineering educational settings. 5 teachers from St. Louis, USA engaged in a co-design workshop to explore an emerging ML toolkit and to consider ways of structuring classroom activities to integrate the technology into their teaching. Using video and post-interview data, we report on how engagement in the workshop activities influenced their confidence. We claim that educators' confidence grew when they were provided with hands-on opportunities to explore and understand emerging technologies. Moreover, our analysis underscores recognizing and validating teachers’ unique insights and perspectives in fostering their confidence. Additionally, we highlight the significance of involving educators in the collaborative design of curricula and activities centered around these innovative ML tools. By shedding light on these critical elements, our research offers practical guidance for fostering a supportive environment that encourages educators to embrace and effectively integrate ML technologies into their engineering teaching practices.

Xu, G., & Dahal, M., & Gravel, B. (2024, June), Exploring K-12 Teachers’ Confidence in Using Machine Learning Emerging Technologies through Co-design Workshop (RTP) Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://strategy.asee.org/47419

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