research-article

Can Children Understand Machine Learning Concepts?: The Effect of Uncovering Black Boxes

Authors:

Oren ZuckermanAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: 415, Pages 1 - 11

https://doi.org/10.1145/3290605.3300645

Published: 02 May 2019 Publication History

Abstract

Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.

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Cited By

Morales-Navarro LKafai Y(2024)Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design ActivitiesProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678117(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678117
Olari VRomeike R(2024)Data-related practices for creating Artificial Intelligence systems in K-12Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678115(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678115
Marx EWitt CLeonhardt T(2024)Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview StudyProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678114(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678114
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Index Terms

Can Children Understand Machine Learning Concepts?: The Effect of Uncovering Black Boxes
1. Applied computing
  1. Education
    1. Interactive learning environments
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
      1. User interface management systems

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cover image ACM Conferences

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Copyright © 2019 ACM.

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Published: 02 May 2019

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Morales-Navarro LKafai Y(2024)Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design ActivitiesProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678117(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678117
Olari VRomeike R(2024)Data-related practices for creating Artificial Intelligence systems in K-12Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678115(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678115
Marx EWitt CLeonhardt T(2024)Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview StudyProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678114(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678114
Höper LSchulte CMühling A(2024)Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing EducationProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671118(326-342)Online publication date: 12-Aug-2024
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Grover SStephenson BStone JBattestilli LRebelsky SShoop L(2024)Teaching AI to K-12 Learners: Lessons, Issues, and GuidanceProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630937(422-428)Online publication date: 7-Mar-2024
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