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Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy Cover

Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy

Citizen Science: Theory and Practice
Volume 9 (2024): Issue 1
By: Carsten Østerlund,  Kevin Crowston,  Corey B. Jackson,  Yunan Wu,  Alexander O. Smith and  Aggelos K. Katsaggelos  
Open Access
|Dec 2024

Figures & Tables

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Figure 1

Individuals’ increase in capability, with the zone of proximal development (ZPD) in the centre of the figure.

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Figure 2

Machine zone of proximal development (ZPD).

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Figure 3

Co-augmentation of human and machine zones of proximal development (ZPD).

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Figure 4

The Gravity Spy classification interface, showing a glitch to be classified on the left and the potential glitch classes on the right.

DOI: https://doi.org/10.5334/cstp.738 | Journal eISSN: 2057-4991
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Language: English
Submitted on: Feb 16, 2024
Accepted on: Aug 22, 2024
Published on: Dec 9, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
learning,
zone of proximal development,
human-artificial intelligence augmentation

© 2024 Carsten Østerlund, Kevin Crowston, Corey B. Jackson, Yunan Wu, Alexander O. Smith, Aggelos K. Katsaggelos, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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