@article {9998,
	title = {Knowledge Tracing to Model Learning in Online Citizen Science Projects},
	journal = {IEEE Transactions on Learning Technologies},
	volume = {13},
	year = {2020},
	pages = {123-134},
	abstract = {<p>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.</p>},
	issn = {1939-1382},
	doi = {10.1109/TLT.2019.2936480 },
	attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/transaction\%20paper\%20final\%20figures\%20in\%20text.pdf},
	author = {Kevin Crowston and Carsten {\O}sterlund and Tae Kyoung Lee and Corey Brian Jackson and Mahboobeh Harandi and Sarah Allen and Sara Bahaadini and Scott Coughlin and Aggelos Katsaggelos and Shane Larson and Neda Rohani and Joshua Smith and Laura Trouille and Michael Zevin}
}
