Knowledge Tracing to Model Learning in Online Citizen Science Projects

被引:11
|
作者
Crowston, Kevin [1 ]
Osterlund, Carsten [1 ]
Lee, Tae Kyoung [2 ]
Jackson, Corey [1 ,3 ]
Harandi, Mahboobeh [1 ]
Allen, Sarah [4 ]
Bahaadini, Sara [5 ]
Coughlin, Scotty [6 ]
Katsaggelos, Aggelos K. [5 ]
Larson, Shane L. [6 ]
Rohani, Neda [5 ]
Smith, Joshua R. [7 ]
Trouille, Laura [4 ]
Zevin, Michael [6 ]
机构
[1] Syracuse Univ, Sch Informat Studies, Syracuse, NY 13244 USA
[2] Univ Utah, Dept Commun, Salt Lake City, UT 84112 USA
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
[4] Adler Planetarium, Chicago, IL 60605 USA
[5] Northwestern Univ, Dept Elect Engn & Comp Sci, Evanston, IL USA
[6] Northwestern Univ, Dept Phys & Astron, CIERA, Evanston, IL 60208 USA
[7] Calif State Univ Fullerton, Dept Phys, Fullerton, CA 92831 USA
来源
基金
美国国家科学基金会;
关键词
Training; Task analysis; Gravity; Machine learning; Data models; Bayes methods; Classification algorithms; Citizen science; machine learning; training;
D O I
10.1109/TLT.2019.2936480
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We present the design of a citizen science system that uses machine learning to guide the presentation of image classification tasks to newcomers to help them more quickly learn how to do the task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning for training with tasks with uncertain outcomes is presented and fit to data from 12,986 volunteer contributors. The model can be used both to estimate the ability of volunteers and to decide the classification of an image. A simulation of the model applied to volunteer promotion and image retirement suggests that the model requires fewer classifications than the current system.
引用
收藏
页码:123 / 134
页数:12
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