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From Demo to Design in Teaching Machine Learning

Authors:

Karl-Emi Kjær Bilstrup,

Magnus Høholt Kaspersen,

Marianne Graves PetersenAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 2168 - 2178

https://doi.org/10.1145/3531146.3534634

Published: 20 June 2022 Publication History

Abstract

The prevalence of artificial intelligence (AI) and machine learning (ML) technologies in digital ecosystems has led to a push for AI literacy, giving everybody, including K-12 students, the necessary knowledge and abilities to engage critically with these new technologies. While there is an increasing focus on designing tools and activities for teaching machine learning, most tools sidestep engaging with the complexity and trade-offs inherent in the design of ML models in favor of demonstrating the power and functionality of the technology. In this paper, we investigate how a design perspective can inform the design of educational tools and activities for teaching machine learning. Through a literature review, we identify 34 tools and activities for teaching ML, and using a design perspective on ML system development, we examine strengths and limitations in how they engage students in the complex design considerations linked to the different components of machine learners. Based on this work, we suggest directions for furthering AI literacy through adopting a design approach in teaching ML.

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

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DOI:10.1145/3531146

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FAccT '22

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ACM

FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency

June 21 - 24, 2022

Seoul, Republic of Korea

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Sebastian PSharma SIivari NKinnula MMonga CVerma DAbbas M(2024)Emerging Technologies in Global South Classrooms: Teachers Imagining Future of EducationProceedings of the Participatory Design Conference 2024: Full Papers - Volume 110.1145/3666094.3666109(234-247)Online publication date: 11-Aug-2024
https://dl.acm.org/doi/10.1145/3666094.3666109
Bilstrup KKaspersen MBouvin NPetersen M(2024)ml-machine.org: Infrastructuring a Research Product to Disseminate AI Literacy in EducationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642539(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642539
Andersen LBasballe DBuus LDindler CHansen THjorth MIversen OJohannessen CKanstrup KLorentzen RMisfeldt MMusaeus LNielsen CPetersen MSchrøder VSlot M(2024)Infrastructuring Digital Literacy in K-12 Education: A National Case StudyInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2024.100697(100697)Online publication date: Oct-2024
https://doi.org/10.1016/j.ijcci.2024.100697
Murray-Rust DLupetti MNicenboim IHoog W(2023)Grasping AI: experiential exercises for designersAI & SOCIETY10.1007/s00146-023-01794-yOnline publication date: 30-Oct-2023
https://doi.org/10.1007/s00146-023-01794-y
Mahon JBecker BNamee B(2023)AI and ML in School Level Computing Education: Who, What and Where?Artificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_16(201-213)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_16
Bilstrup KKaspersen MSørensen MPetersen M(2022)Opportunities and Challenges of Teaching Machine Learning as a Design Material with the micro:bitAdjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference10.1145/3547522.3547689(1-6)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3547522.3547689

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