TY - JOUR T1 - Knowledge Tracing to Model Learning in Online Citizen Science Projects JF - IEEE Transactions on Learning Technologies Y1 - 2020 A1 - Kevin Crowston A1 - Carsten Ă˜sterlund 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.

VL - 13 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 -