%0 Journal Article %J IEEE Transactions on Learning Technologies %D 2020 %T Knowledge Tracing to Model Learning in Online Citizen Science Projects %A Kevin Crowston %A Carsten Ă˜sterlund %A Tae Kyoung Lee %A Corey Brian Jackson %A Mahboobeh Harandi %A Sarah Allen %A Sara Bahaadini %A Scott Coughlin %A Aggelos Katsaggelos %A Shane Larson %A Neda Rohani %A Joshua Smith %A Laura Trouille %A Michael Zevin %X

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.

%B IEEE Transactions on Learning Technologies %V 13 %P 123-134 %G eng %6 1 %R 10.1109/TLT.2019.2936480 %> https://citsci.syr.edu/sites/crowston.syr.edu/files/transaction%20paper%20final%20figures%20in%20text.pdf %0 Journal Article %J Classical and Quantum Gravity %D 2017 %T Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science %A Michael Zevin %A Scott Coughlin %A Sara Bahaadini %A Emre Besler %A Neda Rohani %A Sarah Allen %A Miriam Cabero %A Kevin Crowston %A Aggelos Katsaggelos %A Shane Larson %A Tae Kyoung Lee %A Chris Lintott %A Tyson Littenberg %A Andrew Lundgren %A Carsten Oesterlund %A Joshua Smith %A Laura Trouille %A Vicky Kalogera %B Classical and Quantum Gravity %V 34 %P 064003 %G eng %9 Journal Article %R 10.1088/1361-6382/aa5cea