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ML education for K-12: emerging trajectories

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  • UserMatti Tedre and Henriikka Vartiainen (University of Eastern Finland)
  • ClockTuesday 02 November 2021, 17:00-18:30
  • HouseVenue to be confirmed.

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Over the past decades, practical applications of machine learning (ML) techniques have shown the potential of data-driven approaches in computing. ML education has been primarily piloted in computing curricula in higher education, but increasingly in K-12 computing education, too. However, despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space.

This talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. It will situate that research in the broader context of computing education, and describe what changes ML necessitates in the classroom. The talk will outline the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning many tenets of rule-based “classical” programming.


Dr. Matti Tedre is a professor of computer science, especially computing education and the philosophy of computer science, at the University of Eastern Finland. His 2019 book “Computational Thinking” (The MIT Press, with P.J. Denning) presented a rich picture of computing’s disciplinary ways of thinking and practicing, and his 2014 book “Science of Computing” (Taylor & Francis / CRC Press) portrayed the conceptual and technical history of computing as a discipline.

Dr. Henriikka Vartiainen is a senior researcher and university lecturer at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. She has also worked as responsible researcher in several multidisciplinary projects focusing on, for example, technology education, co-design in school context, design-oriented pedagogy, and 21st skills. Currently, her research focuses especially on learning Machine Learning through co-design as well as on the ways to support children’s data agency. Her work on design-oriented pedagogy has received The Doctoral Dissertation Award 2014 by The Finnish Educational Research Association (FERA) as well as Young Researcher Award of the UEF in 2015.

This talk is part of the Computing Education Research series.

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