TY - JOUR
T1 - Knowledge Tracing to Model Learning in Online Citizen Science Projects
Y1 - Submitted
A1 - Kevin Crowston
A1 - Ă˜sterlund, Carsten
A1 - Tae Kyoung Lee
A1 - Corey Brian Jackson
A1 - Mahboobeh Harandi
A1 - Sarah Allen
A1 - Sara Bahaadini
A1 - Scott Coughlin
A1 - Aggelos Katsaggelos
A1 - Shane Larson
A1 - Neda Rohani
A1 - Joshua Smith
A1 - Laura Trouille
A1 - Michael Zevin
AB - 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.
ER -
TY - JOUR
T1 - Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
JF - Classical and Quantum Gravity
Y1 - 2017
A1 - Michael Zevin
A1 - Scott Coughlin
A1 - Sara Bahaadini
A1 - Emre Besler
A1 - Neda Rohani
A1 - Sarah Allen
A1 - Miriam Cabero
A1 - Kevin Crowston
A1 - Aggelos Katsaggelos
A1 - Shane Larson
A1 - Tae Kyoung Lee
A1 - Chris Lintott
A1 - Tyson Littenberg
A1 - Andrew Lundgren
A1 - Carsten Oesterlund
A1 - Joshua Smith
A1 - Laura Trouille
A1 - Vicky Kalogera
VL - 34
ER -