extended-abstract

Promoting Machine Learning Concept to Young Learners in a National Science Fair

Authors:

Ismaila Temitayo Sanusi,

Ilkka Jormanainen,

Solomon Sunday Oyelere,

Vaishali Mahipal,

Fred MartinAuthors Info & Claims

Koli Calling '22: Proceedings of the 22nd Koli Calling International Conference on Computing Education Research

Article No.: 35, Pages 1 - 2

https://doi.org/10.1145/3564721.3565961

Published: 17 November 2022 Publication History

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Abstract

There is a growing number of initiatives for teaching artificial intelligence or machine learning in the compulsory levels of education. However, more research and development is required to understand technological and pedagogical aspects of AI teaching especially in K-12 level. In the context of a two day workshop in a science festival, we introduced the concept of Convolution neural network (CNN) and examined how children learn about the way CNN performs image recognition. The concept was presented through hands-on practice with DoodleIt, a simple app for introducing the fundamental ideas behind CNN.

References

[1]

Musa Adekunle Ayanwale, Ismaila Temitayo Sanusi, Owolabi Paul Adelana, Kehinde D Aruleba, and Solomon Sunday Oyelere. 2022. Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence (2022), 100099.

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[2]

Thomas KF Chiu. 2021. A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends 65, 5 (2021), 796–807.

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Google Creative Lab. 2017. Quick, Draw!https://experiments.withgoogle.com/quick-draw

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Vaishali Mahipal. 2022. DoodleIt: Introducing Middle School Students to Image Recognition.

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Ismaila Temitayo Sanusi, Solomon Sunday Oyelere, Friday Joseph Agbo, and Jarkko Suhonen. 2021. Survey of resources for introducing machine learning in K-12 context. In 2021 IEEE Frontiers in Education Conference (FIE). IEEE, 1–9.

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Tapani Toivonen, Ilkka Jormanainen, Matti Tedre, Radu Mariescu-Istodor, Teemu Valtonen, Henriikka Vartiainen, and Juho Kahila. 2022. Interacting By Drawing: Introducing Machine Learning Ideas to Children at a K–9 Science Fair. In CHI Conference on Human Factors in Computing Systems Extended Abstracts. 1–5.

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Henriikka Vartiainen, Matti Tedre, and Teemu Valtonen. 2020. Learning machine learning with very young children: Who is teaching whom?International journal of child-computer interaction 25 (2020), 100182.

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Qi Xia, Thomas KF Chiu, Min Lee, Ismaila Temitayo Sanusi, Yun Dai, and Ching Sing Chai. 2022. A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education 189 (2022), 104582.

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Cited By

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Sanusi IMartin FMa RGonzales JMahipal VOyelere SSuhonen JTukiainen M(2024)AI MyData: Fostering Middle School Students’ Engagement with Machine Learning through an Ethics-Infused AI CurriculumACM Transactions on Computing Education10.1145/370224224:4(1-37)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3702242
Sanusi IOmidiora JOyelere SVartiainen HSuhonen JTukiainen M(2023)Preparing Middle Schoolers for a Machine Learning-Enabled Future Through Design-Oriented PedagogyIEEE Access10.1109/ACCESS.2023.326902511(39776-39791)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269025

Index Terms

Promoting Machine Learning Concept to Young Learners in a National Science Fair
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

Koli Calling '22: Proceedings of the 22nd Koli Calling International Conference on Computing Education Research

November 2022

282 pages

ISBN:9781450396165

DOI:10.1145/3564721

Editors:
Ilkka Jormanainen
University of Eastern Finland, Finland
,
Andrew Petersen
University of Toronto Mississauga, Canada

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 17 November 2022

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Koli 2022

Koli 2022: 22nd Koli Calling International Conference on Computing Education Research

November 17 - 20, 2022

Koli, Finland

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Sanusi IMartin FMa RGonzales JMahipal VOyelere SSuhonen JTukiainen M(2024)AI MyData: Fostering Middle School Students’ Engagement with Machine Learning through an Ethics-Infused AI CurriculumACM Transactions on Computing Education10.1145/370224224:4(1-37)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3702242
Sanusi IOmidiora JOyelere SVartiainen HSuhonen JTukiainen M(2023)Preparing Middle Schoolers for a Machine Learning-Enabled Future Through Design-Oriented PedagogyIEEE Access10.1109/ACCESS.2023.326902511(39776-39791)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269025

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Interacting By Drawing: Introducing Machine Learning Ideas to Children at a K–9 Science Fair

A Blended Learning Model: Results of Young Learners

How we taught AI concepts, workflows, and ethics to 200 schoolchildren

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