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Data Detectives: A Tabletop Card Game about Training Data

Published: 07 July 2022 Publication History

Abstract

Youth regularly interface with AI technology that leverages supervised machine learning. However, it is well-known that biased training data can result in harmful algorithmic bias. Thus, it is important that youth and families understand training data in machine learning. We present Data Detectives, a child-friendly tabletop card game about training data. Based on three research-based design principles: low-stakes experimentation to support curiosity, games facilitating conversation, and tangible and embodied learning for abstract concepts, the game supports learning the high-level mechanics of training data in supervised machine learning, as well as practicing critical discussion of training data related to algorithmic bias. Contributing to AI literacy opportunities, this game aims to facilitate playful peer-peer and child-parent learning.

References

[1]
George Palaigeorgiou, Dimitra Tsapkini, Tharrenos Bratitsis, and Stefanos Xefteris. 2017. Embodied learning about time with tangible clocks. In Interactive Mobile Communication, Technologies and Learning. Springer, 477--486.
[2]
Jaemarie Solyst, Alexis Axon, Angela Stewart, Motahhare Eslami, and Amy Ogan. forthcoming. Investigating Girls' Perspectives and Knowledge Gaps on Ethics and Fairness in Artificial Intelligence in a Lightweight Workshop. International Conference of the Learning Sciences Annual Meeting 2022 ( forthcoming).
[3]
Alexandra To, Jarrek Holmes, Elaine Fath, Eda Zhang, Geoff Kaufman, and Jessica Hammer. 2018. Modeling and designing for key elements of curiosity: Risking failure. Transactions of the Digital Games Research Association, Vol. 4, 2 (2018).

Cited By

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  • (2023)How to Playfully Teach AI to Young Learners: a Systematic Literature ReviewProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter10.1145/3605390.3605393(1-9)Online publication date: 20-Sep-2023

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  1. Data Detectives: A Tabletop Card Game about Training Data

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    cover image ACM Conferences
    ITiCSE '22: Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2
    July 2022
    686 pages
    ISBN:9781450392006
    DOI:10.1145/3502717
    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

    Publication History

    Published: 07 July 2022

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    Author Tags

    1. artificial intelligence literacy
    2. computer science education
    3. game
    4. k-12
    5. training data

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    Overall Acceptance Rate 552 of 1,613 submissions, 34%

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    • (2023)How to Playfully Teach AI to Young Learners: a Systematic Literature ReviewProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter10.1145/3605390.3605393(1-9)Online publication date: 20-Sep-2023

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