Literaturnachweis - Detailanzeige
| Autor/inn/en | Crowston, Kevin; Østerlund, Carsten; Lee, Tae Kyoung; Jackson, Corey; Harandi, Mahboobeh; Allen, Sarah; Bahaadini, Sara; Coughlin, Scott; Katsaggelos, Aggelos K.; Larson, Shane L.; Rohani, Neda; Smith, Joshua R.; Trouille, Laura; Zevin, Michael |
|---|---|
| Titel | Knowledge Tracing to Model Learning in Online Citizen Science Projects |
| Quelle | In: IEEE Transactions on Learning Technologies, 13 (2020) 1, S.123-134 (12 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Crowston, Kevin) ORCID (Østerlund, Carsten) ORCID (Jackson, Corey) ORCID (Zevin, Michael) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1939-1382 |
| DOI | 10.1109/TLT.2019.2936480 |
| Schlagwörter | Citizen Participation; Scientific Research; Man Machine Systems; Training; Models; Task Analysis; Volunteers; Bayesian Statistics; Artificial Intelligence; Simulation; Instructional Design |
| 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. (As Provided). |
| Anmerkungen | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076 |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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