abstract

Scratch Nodes ML: A Playful System for Children to Create Gesture Recognition Classifiers

Authors:

Adam Agassi,

Hadas Erel,

Iddo Yehoshua Wald,

Oren ZuckermanAuthors Info & Claims

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: LBW0111, Pages 1 - 6

https://doi.org/10.1145/3290607.3312894

Published: 02 May 2019 Publication History

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Abstract

Children are growing up in a Machine Learning infused world and it's imperative to provide them with opportunities to develop an accurate understanding of basic Machine Learning concepts. Physical gesture recognition is a typical application of Machine Learning, and physical gestures are also an integral part of children's lives, including sports and play. We present Scratch Nodes ML, a system enabling children to create personalized gesture recognizers by: (1) Creating their own gesture classes; (2) Collecting gesture data for each class; (3) Evaluating the classifier they created with new gesture data; (4) Integrating their classifiers into the Scratch environment as new Scratch blocks, empowering other children to use these new blocks as gesture classifiers in their own Scratch creations.

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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
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Tseng TDavidson MMorales-Navarro LChen JDelaney VLeibowitz MBeason JShapiro R(2024)Co-ML: Collaborative Machine Learning Model Building for Developing Dataset Design PracticesACM Transactions on Computing Education10.1145/364155224:2(1-37)Online publication date: 16-Apr-2024
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Index Terms

Scratch Nodes ML: A Playful System for Children to Create Gesture Recognition Classifiers
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Social and professional topics
  1. User characteristics
    1. Age
      1. Children

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Published In

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

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

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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Published: 02 May 2019

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CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Arn LHuang EPak DYeh TSchwartz L(2024)"Do They Act the Way They Look"Proceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685337(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685337
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
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A gesture-based virtual art program for children with severe motor impairments: development and pilot study

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