ABSTRACT
In the 2010s machine learning (ML) became a key driver of quickly growing number of apps and services. How to teach ML and other data-driven approaches in K–12 education has become a focus of intensive research efforts, with many recent advances in technology, pedagogy, and classroom integration. What to teach learners, at what age, and how, are some of the open questions being explored.
This study explored children’s interactions with a simple image classification tool, which used only two features to classify images. The results offer a proof-of-concept of how to teach 1) the principles of the ML workflow, and 2) some central ML insights, including image recognition, supervised learning, training data, model, feature, classifying, and accuracy. The results recognize how learning the principles of technology facilitates a shift in the locus of explanation from what oneself does to what the computer does. The results provide examples of how to support children’s developing data agency.
- Safinah Ali, Blakeley H Payne, Randi Williams, Hae Won Park, and Cynthia Breazeal. 2019. Constructionism, Ethics, and Creativity: Developing Primary and Middle School Artificial Intelligence Education. In International Workshop on Education in Artificial Intelligence K–12 (EDUAI’19). Chicago, IL, USA.Google Scholar
- Stefania Druga. 2018. Growing up with AI: Cognimates: from Coding to Teaching Machines. Master’s thesis. Massachusetts Institute of Technology.Google Scholar
- Ignacio Evangelista, Germán Blesio, and Emanuel Benatti. 2018. Why Are We Not Teaching Machine Learning at High School? A Proposal. In 2018 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC). IEEE, 1–6.Google Scholar
- Fredrik Heintz and Teemu Roos. 2021. Elements Of AI - Teaching the Basics of AI to Everyone in Sweden. In Proceedings of the 13th International Conference on Education and New Learning Technologies (EDULEARN21). IATED, Online, 2568–2572. https://doi.org/10.21125/edulearn.2021.0559Google ScholarCross Ref
- Arne Hintz, Karin Wahl-Jorgensen, and Lina Dencik. 2018. Digital Citizenship in a Datafied Society. Polity Press, Cambridge, UK.Google Scholar
- Joshua W. K. Ho and Matthew Scadding. 2019. Classroom Activities for Teaching Artificial Intelligence to Primary School Students. In Proceedings of International Conference on Computational Thinking Education 2019, S.C. Kong, D. Andone, G. Biswas, H.U. Hoppe, T.C. Hsu, R.H. Huang, B.C. Kuo, K.Y. Li, C.K. Looi, M. Milrad, J. Sheldon, J.L. Shih, K.F. Sin, K.S. Song, and J. Vahrenhold (Eds.). The Education University of Hong Kong, Hong Kong, 157–159.Google Scholar
- Kenneth Kahn, Rani Megasari, Erna Piantari, and Enjun Junaeti. 2018. AI Programming by Children using Snap! Block Programming in a Developing Country. In EC-TEL Practitioner Proceedings 2018: 13th European Conference On Technology Enhanced Learning, Vania Dimitrova, Sambit Praharaj, Mikhail Fominykh, and Hendrik Drachsler (Eds.). Leeds, UK.Google Scholar
- Martin Kandlhofer, Gerald Steinbauer, Sabine Hirschmugl-Gaisch, and Petra Huber. 2016. Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE). 1–9. https://doi.org/10.1109/FIE.2016.7757570Google ScholarDigital Library
- Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). ACM, New York, NY, USA, 1–16. https://doi.org/10.1145/3313831.3376727Google ScholarDigital Library
- Radu Mariescu-Istodor and Ilkka Jormanainen. 2019. Machine Learning for High School Students. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’19). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3364510.3364520Google ScholarDigital Library
- Lívia S. Marques, Christiane Gresse von Wangenheim, and Jean C. R. Hauck. 2020. Teaching Machine Learning in School: A Systematic Mapping of the State of the Art. Informatics in Education 19, 2 (2020), 283–321. https://doi.org/10.15388/infedu.2020.14Google ScholarCross Ref
- Mitchel Resnick and Brian Silverman. 2005. Some Reflections on Designing Construction Kits for Kids. In Proceedings of the 2005 Conference on Interaction Design and Children (Boulder, CO, USA) (IDC ’05). ACM, New York, NY, USA, 117–122. https://doi.org/10.1145/1109540.1109556Google ScholarDigital Library
- Barbara Rogoff. 1990. Apprenticeship in Thinking: Cognitive Development in Social Context. Oxford University Press, New York, NY, USA.Google Scholar
- Michael Schlichtig, Simone Opel, Lea Budde, and Carsten Schulte. 2019. Understanding Artificial Intelligence — A Project for the Development of Comprehensive Teaching Material. In Proceedings of the 12th International conference on informatics in schools (ISSEP 2019): Situation, evaluation and perspectives, Eglė Jasutė and Sergei Pozdniakov (Eds.). 65–73.Google Scholar
- R. Benjamin Shapiro, Rebecca Fiebrink, and Peter Norvig. 2018. How Machine Learning Impacts the Undergraduate Computing Curriculum. Commun. ACM 61, 11 (2018), 27–29.Google ScholarDigital Library
- Ahuva Sperling and Dorit Lickerman. 2012. Integrating AI and Machine Learning in Software Engineering Course for High School Students. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (Haifa, Israel) (ITiCSE ’12). ACM, New York, NY, USA, 244–249. https://doi.org/10.1145/2325296.2325354Google ScholarDigital Library
- Danny Tang. 2019. Empowering Novices to Understand and Use Machine Learning With Personalized Image Classification Models, Intuitive Analysis Tools, and MIT App Inventor. Master’s thesis. Massachusetts Institute of Technology.Google Scholar
- Matti Tedre, Peter J. Denning, and Tapani Toivonen. 2021. CT 2.0. In 21st Koli Calling International Conference on Computing Education Research (Joensuu, Finland) (Koli Calling ’21). ACM, New York, NY, USA, Article 3, 8 pages. https://doi.org/10.1145/3488042.3488053Google ScholarDigital Library
- Matti Tedre, Tapani Toivonen, Juho Kahila, Henriikka Vartiainen, Teemu Valtonen, Ilkka Jormanainen, and Arnold Pears. 2021. Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education. IEEE Access 9(2021), 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962Google ScholarCross Ref
- Matti Tedre, Henriikka Vartiainen, Juho Kahila, Tapani Toivonen, Ilkka Jormanainen, and Teemu Valtonen. 2020. Machine Learning Introduces New Perspectives to Data Agency in K–12 Computing Education. In 2020 IEEE Frontiers in Education Conference (FIE). 1–8. https://doi.org/10.1109/FIE44824.2020.9274138Google ScholarCross Ref
- David Touretzky, Christina Gardner-McCune, Fred Martin, and Deborah Seehorn. 2019. Envisioning AI for K-12: What Should Every Child Know about AI?. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 1. Association for the Advancement of Artificial Intelligence, 9795–9799.Google ScholarDigital Library
- Henriikka Vartiainen, Tapani Toivonen, Ilkka Jormanainen, Juho Kahila, Matti Tedre, and Teemu Valtonen. 2021. Machine Learning for Middle Schoolers: Learning through Data-Driven Design. International Journal of Child-Computer Interaction 29 (2021), 100281.Google ScholarDigital Library
- Randi Williams, Hae Won Park, and Cynthia Breazeal. 2019. A is for Artificial Intelligence: The Impact of Artificial Intelligence Activities on Young Children’s Perceptions of Robots. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). ACM, New York, NY, USA, 1–11. https://doi.org/10.1145/3290605.3300677Google ScholarDigital Library
- Xiaofei Zhou, Jessica Van Brummelen, and Phoebe Lin. 2020. Designing AI Learning Experiences for K–12: Emerging Works, Future Opportunities and a Design Framework. arxiv:2009.10228 [cs.CY]Google Scholar
- Abigail Zimmermann-Niefield, Makenna Turner, Bridget Murphy, Shaun K. Kane, and R. Benjamin Shapiro. 2019. Youth Learning Machine Learning through Building Models of Athletic Moves. In Proceedings of the 18th ACM International Conference on Interaction Design and Children(Boise, ID, USA) (IDC ’19). ACM, New York, NY, USA, 121–132. https://doi.org/10.1145/3311927.3323139Google ScholarDigital Library
Index Terms
- Interacting By Drawing: Introducing Machine Learning Ideas to Children at a K–9 Science Fair
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