Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41159

Blending Machine and Human Learning Processes

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dc.contributor.author Crowston, Kevin
dc.contributor.author Østerlund, Carsten
dc.contributor.author Lee, Tae Kyoung
dc.date.accessioned 2016-12-29T00:07:45Z
dc.date.available 2016-12-29T00:07:45Z
dc.date.issued 2017-01-04
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41159
dc.description.abstract Citizen science projects face a dilemma in relying on contributions from volunteers to achieve their scientific goals: providing volunteers with explicit training might increase the quality of contributions, but at the cost of losing the work done by newcomers during the training period, which for many is the only work they will contribute to the project. Based on research in cognitive science on how humans learn to classify images, we have designed an approach to use machine learning to guide the presentation of tasks to newcomers that help them more quickly learn how to do the image classification task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning is presented.
dc.format.extent 9 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Citizen science
dc.subject training
dc.subject Bayesian knowledge tracing
dc.title Blending Machine and Human Learning Processes
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2017.009
Appears in Collections: Advances in Teaching and Learning Minitrack


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