Please use this identifier to cite or link to this item:

Blending Machine and Human Learning Processes

File SizeFormat 
paper0010.pdf2.95 MBAdobe PDFView/Open

Item Summary

Title: Blending Machine and Human Learning Processes
Authors: Crowston, Kevin
Østerlund, Carsten
Lee, Tae Kyoung
Keywords: Citizen science
Bayesian knowledge tracing
Issue Date: 04 Jan 2017
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.
Pages/Duration: 9 pages
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.009
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Advances in Teaching and Learning Minitrack

Please contact if you need this content in an alternative format.

Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.