Papers
. (2020). Knowledge Tracing to Model Learning in Online Citizen Science Projects. Ieee Transactions On Learning Technologies, 13, 123-134. https://doi.org/10.1109/TLT.2019.2936480
transaction paper final figures in text.pdf (1.39 MB) Novelty_Motivator_2020.pdf (1.15 MB). (2020). Shifting forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings Of The Acm On Human-Computer Interaction, (CSCW), 36. https://doi.org/10.1145/3392841
3392841.pdf (1.13 MB). (2020). Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training. Computers In Human Behavior, 105, 106198. https://doi.org/10.1016/j.chb.2019.106198
MLGT-preprint.pdf (2.43 MB). (2021). Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Classical And Quantum Gravity, 38(19). https://doi.org/10.1088/1361-6382/ac1ccb
. (2021). Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research. In A Computing Community Consortium (CCC) Quadrennial Paper. Retrieved de https://cra.org/ccc/wp-content/uploads/sites/2/2021/03/CCC-TransitionPaperImagine-All-the-People.pdf
. (2021). Occasional Groups in Crowdsourcing Platforms. In School of Information Studies. Syracuse University, Syracuse, NY, USA.
Dissertation_MH.pdf (3.92 MB). (2022). Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications. https://doi.org/10.48550/ARXIV.2208.12849
. (2023). Design principles for background knowledge to enhance learning in citizen science. In iConference.
Design_Background_iConf.pdf (3.78 MB). (In Press). Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications. Classical And Quantum Gravity. https://doi.org/10.48550/ARXIV.2208.12849
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