Anna Julia Postdoc @ University of Wisconsin, Madison

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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About me

Posts

Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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portfolio

publications

Planet hunters and seafloor explorers: legitimate peripheral participation through practice proxies in online citizen science

Published in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, 2014

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Recommended citation: Mugar, Gabriel, Carsten Østerlund, Katie DeVries Hassman, Kevin Crowston, and Corey Jackson. (2014). "Planet hunters and seafloor explorers: legitimate peripheral participation through practice proxies in online citizen science." In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing https://doi.org/10.1145/2531602.2531721

Motivations for sustained participation in crowdsourcing: case studies of citizen science on the role of talk

Published in Forty-eighth Hawaii International Conference on System Sciences, 2015

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Recommended citation: Jackson, Corey, Carsten Østerlund, Gabriel Mugar, Katie DeVries Hassman, and Kevin Crowston. (2015) "Motivations for sustained participation in crowdsourcing: case studies of citizen science on the role of talk." 48th Hawaii International Conference on System Sciences, (HICSS). https://doi.org/10.1109/HICSS.2015.196

Guess what! You’re the First to See this Event Increasing Contribution to Online Production Communities

Published in International Conference on Supporting Group Work (GROUP), 2016

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Recommended citation: Jackson, Corey, Kevin Crowston, Gabriel Mugar, and Carsten Østerlund. (2016). "Guess what! You're the First to See this Event Increasing Contribution to Online Production Communities." Proceedings of the 19th International Conference on Supporting Group Work (GROUP). https://doi.org/10.1145/2957276.2957284

Knowledge Tracing to Model Learning in Online Citizen Science Projects

Published in IEEE Transactions on Learning Technologies, 2019

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Recommended citation: Crowston, Kevin, Carsten Østerlund, Tae Kyoung Lee, Corey Jackson, Mahboobeh Harandi, Sarah Allen, Sara Bahaadini, Scotty Coughlin, Aggelos Katsaggelos, Shane Larson, Neda Rohani, Joshua Smith, Laura Trouille, Michael Zevin. (2019). "Knowledge Tracing to Model Learning in Online Citizen Science Projects." IEEE Transactions on Learning Technologies . https://ieeexplore.ieee.org/document/8812979

Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning

Published in Physical Review D, 2019

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Recommended citation: Coughlin, Scotty, Sara Bahaadini, Neda Rohani, Michael Zevin, Oli Patane, Mahboobeh Harandi, Corey Jackson, Vahid Noroozi, Sarah Allen, Joseph Areeda, Michael Coughlin, Pablo Ruiz, Christopher Berry, Kevin Crowston, Aggelos Katsaggelos, Andrew Lundgren, Carsten ¯sterlund, Joshua Smith, Laura Trouille, and Vicky Kalogera. (2019). "Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning." Physical Review D , 99(8). https://arxiv.org/abs/1903.04058

The Genie in the Bottle: Different Stakeholders, Different Interpretations

Published in Fifty-third Hawai’i International Conference on System Sciences (HICSS-53), 2020

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Recommended citation: Harandi, Mahboobeh, Kevin Crowston, Corey Jackson, Carsten Østerlund. (2020). "The Genie in the Bottle: Different Stakeholders, Different Interpretations." Fifty-third Hawaii International Conference on System Sciences (HICSS-53). https://scholarspace.manoa.hawaii.edu/handle/10125/64461

Teaching citizen scientists to categorize glitches using machine learning guided training

Published in Computers in Human Behavior, 2020

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Recommended citation: Jackson, Corey, Carsten Østerlund, Kevin Crowston, Mahboobeh Harandi, Sarah Allen, Sara Bahaadini, Scotty Coughlin, Vicky Kalogera, Aggelos Katsaggelos, Shane Larson, Neda Rohani, Joshua Smith, Laura Trouille, Michael Zevin. (2020) "Teaching citizen scientists to categorize glitches using machine learning guided training". Computers in Human Behavior , 105(106198). https://www.sciencedirect.com/science/article/pii/S0747563219304182

talks

Citizen Science: Understanding Online User Behaviors and Technologies

Published:

The success of online crowdsourcing and peer production platforms depends on the voluntary participation of people to make contributions – in Wikipedia people write and edit articles, in free/libre open-source software (FLOSS) projects people compile software code and documentation, and in online citizen science projects people assist scientists in the collection and analysis of scientific datasets. A persistent problem plaguing crowdsourcing and peer production platforms is encouraging people to join, and once they join to remain active contributors. The research reports on the findings of an online field experiment where novelty cues were used to motivate volunteers. Novelty is a characteristic of objects and environments frequently associated with increased human attention; however, a dearth of empirical research exists exploring its effects on human behaviors in online settings. I provide an overview of novelty unpacking its theoretical origins, neurological processes, physiological responses, and the results of an experiment where the behaviors of volunteers to three citizen science projects were assessed. Such descriptions are necessary to understand the basis, mechanisms, and purposes of novelty and its effects on human behavior.

Advancing Citizen Science Research: Learning, Motivation, and Beyond

Published:

The arc of scientific research points towards an ever-growing universe of new automated scientific-data collection instruments, increasing the amount and variety of data. Virtual citizen science projects are a popular method for addressing the deluge of data generated by such instruments and facilitate collaboration on large-scale scientific research between professional scientists and amateur volunteers. Learning and motivation are two critical areas of research dictating the success of virtual citizen projects. Since most citizen scientists are amateur volunteers with little knowledge about the technical infrastructure supporting their participation or the science subject matter, insights into how citizen scientists acquire procedural knowledge, technical competence, and learn the subject matter is essential.

Advancing Citizen Science Research: Learning, Motivation, and Beyond

Published:

The arc of scientific research points towards an ever-growing universe of new automated scientific-data collection instruments, increasing the amount and variety of data. Virtual citizen science projects are a popular method for addressing the deluge of data generated by such instruments and facilitate collaboration on large-scale scientific research between professional scientists and amateur volunteers. Learning and motivation are two critical areas of research dictating the success of virtual citizen projects. Since most citizen scientists are amateur volunteers with little knowledge about the technical infrastructure supporting their participation or the science subject matter, insights into how citizen scientists acquire procedural knowledge, technical competence, and learn the subject matter is essential.

teaching

IST 687 Introduction to Data Science

Masters course, Syracuse University, School of Information Studies, 2021

The course provides students a hands-on introduction to data science, with applied examples of data collection, processing, transformation, management and analysis. Students will explore key concepts related to data science, including applied statistics, information visualization, text mining and machine learning. R, the open source statistical analysis and visualization system, will be used throughout the course. R is reckoned by many to be the most popular choice among data analysts worldwide; having knowledge and skill with using it is considered a valuable and marketable job skill for most data scientists. Students will also learn how to use supervised and unsupervised machine learning techniques. They will focus on structured data, using R (e.g., support vector machines, association rules mining) in conjunction with learning the full life cycle of data science.

Research Design and Applications for Data Analysis (RDADA)

Masters course, University of California, Berkeley, School of Information, 2021

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.