Knowledge Tracing to Model Learning in Online Citizen Science Projects. Crowston, K., Osterlund, C., Lee, T. K., Jackson, C., Harandi, M., Allen, S., Bahaadini, S., Coughlin, S., Katsaggelos, A. K., Larson, S. L., Rohani, N., Smith, J. R., Trouille, L., & Zevin, M. IEEE Transactions on Learning Technologies, 13(1):123–134, jan, 2020. ![Knowledge Tracing to Model Learning in Online Citizen Science Projects [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{Kevin2019,
abstract = {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.},
author = {Crowston, Kevin and Osterlund, Carsten and Lee, Tae Kyoung and Jackson, Corey and Harandi, Mahboobeh and Allen, Sarah and Bahaadini, Sara and Coughlin, Scott and Katsaggelos, Aggelos K. and Larson, Shane L. and Rohani, Neda and Smith, Joshua R. and Trouille, Laura and Zevin, Michael},
doi = {10.1109/TLT.2019.2936480},
issn = {1939-1382},
journal = {IEEE Transactions on Learning Technologies},
keywords = {Citizen science,machine learning,training},
month = {jan},
number = {1},
pages = {123--134},
title = {{Knowledge Tracing to Model Learning in Online Citizen Science Projects}},
url = {https://ieeexplore.ieee.org/document/8812979/},
volume = {13},
year = {2020}
}
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