Knowledge Tracing to Model Learning in Online Citizen Science Projects

Kevin Crowston, Shane L. Larson, Neda Rohani, Joshua R. Smith, Laura Trouille, Michael Zevin, Carsten Osterlund, Tae Kyoung Lee, Corey Jackson, Mahboobeh Harandi, Sarah Allen, Sara Bahaadini, Scott Coughlin, Aggelos K. Katsaggelos

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Article number8812979
Pages (from-to)123-134
Number of pages12
JournalIEEE Transactions on Learning Technologies
Volume13
Issue number1
DOIs
StatePublished - Jan 1 2020

Keywords

  • Citizen science
  • machine learning
  • training

ASJC Scopus subject areas

  • Education
  • Engineering(all)
  • Computer Science Applications

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  • Cite this

    Crowston, K., Larson, S. L., Rohani, N., Smith, J. R., Trouille, L., Zevin, M., Osterlund, C., Lee, T. K., Jackson, C., Harandi, M., Allen, S., Bahaadini, S., Coughlin, S., & Katsaggelos, A. K. (2020). Knowledge Tracing to Model Learning in Online Citizen Science Projects. IEEE Transactions on Learning Technologies, 13(1), 123-134. [8812979]. https://doi.org/10.1109/TLT.2019.2936480