Knowledge Tracing to Model Learning in Online Citizen Science Projects

被引:11
|
作者
Crowston, Kevin [1 ]
Osterlund, Carsten [1 ]
Lee, Tae Kyoung [2 ]
Jackson, Corey [1 ,3 ]
Harandi, Mahboobeh [1 ]
Allen, Sarah [4 ]
Bahaadini, Sara [5 ]
Coughlin, Scotty [6 ]
Katsaggelos, Aggelos K. [5 ]
Larson, Shane L. [6 ]
Rohani, Neda [5 ]
Smith, Joshua R. [7 ]
Trouille, Laura [4 ]
Zevin, Michael [6 ]
机构
[1] Syracuse Univ, Sch Informat Studies, Syracuse, NY 13244 USA
[2] Univ Utah, Dept Commun, Salt Lake City, UT 84112 USA
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
[4] Adler Planetarium, Chicago, IL 60605 USA
[5] Northwestern Univ, Dept Elect Engn & Comp Sci, Evanston, IL USA
[6] Northwestern Univ, Dept Phys & Astron, CIERA, Evanston, IL 60208 USA
[7] Calif State Univ Fullerton, Dept Phys, Fullerton, CA 92831 USA
来源
基金
美国国家科学基金会;
关键词
Training; Task analysis; Gravity; Machine learning; Data models; Bayes methods; Classification algorithms; Citizen science; machine learning; training;
D O I
10.1109/TLT.2019.2936480
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:123 / 134
页数:12
相关论文
共 50 条
  • [31] An Innovative Online Tool to Self-evaluate and Compare Participatory Research Projects Labelled as Science Shops or Citizen Science
    Gresle, Anne-Sophie
    Cigarini, Anna
    de la Torre Avila, Leonardo
    Jimeno, Irene
    Bagnoli, Franco
    Dempere, Herman
    Ribera, Mireia
    Puertas, Eloi
    Perello, Josep
    Jesus Pinazo, Maria
    INTERNET SCIENCE, INSCI 2019, 2019, 11938 : 59 - 72
  • [32] How the type and valence of feedback information influence volunteers' knowledge contribution in citizen science projects
    Tang, Jian
    Zhou, Xinxue
    Zhao, Yuxiang
    Wang, Tianmei
    INFORMATION PROCESSING & MANAGEMENT, 2021, 58 (05)
  • [33] ONLINE PROJECTS AS A FORM OF SPREADING PEDAGOGICAL BIOGRAPHICAL KNOWLEDGE IN THE CONTEXT OF OPEN SCIENCE
    Berezivska, Larysa D.
    Mikhno, Oleksandr P.
    Pinchuk, Olha P.
    INFORMATION TECHNOLOGIES AND LEARNING TOOLS, 2023, 97 (05) : 227 - 243
  • [34] A Double machine learning trend model for citizen science data
    Fink, Daniel
    Johnston, Alison
    Strimas-Mackey, Matt
    Auer, Tom
    Hochachka, Wesley M.
    Ligocki, Shawn
    Jaromczyk, Lauren Oldham
    Robinson, Orin
    Wood, Chris
    Kelling, Steve
    Rodewald, Amanda D.
    METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (09): : 2435 - 2448
  • [35] Knowledge management through learning model in industrial projects
    Aramo-Immonen, Heli
    INTERNATIONAL JOURNAL OF KNOWLEDGE AND LEARNING, 2012, 8 (3-4) : 298 - 312
  • [36] 'Citizen identification': online learning supports highly accurate species identification for insect-focussed citizen science
    Perry, Jessica R.
    Sumner, Seirian
    Thompson, Cris
    Hart, Adam G.
    INSECT CONSERVATION AND DIVERSITY, 2021, 14 (06) : 862 - 867
  • [37] Learning in Citizen Science: The Effects of Different Participation Opportunities on Students' Knowledge and Attitudes
    Berndt, Josephine
    Nitz, Sandra
    SUSTAINABILITY, 2023, 15 (16)
  • [38] A model for knowledge innovation in online learning community
    Zhan, Qinglong
    TECHNOLOGIES FOR E-LEARNING AND DIGITAL ENTERTAINMENT, PROCEEDINGS, 2008, 5093 : 21 - 31
  • [39] Authentic science with citizen science and student-driven science fair projects
    Koomen, Michele Hollingsworth
    Rodriguez, Elizabeth
    Hoffman, Alissa
    Petersen, Cindy
    Oberhauser, Karen
    SCIENCE EDUCATION, 2018, 102 (03) : 593 - 644
  • [40] Knowledge structure enhanced graph representation learning model for attentive knowledge tracing
    Gan, Wenbin
    Sun, Yuan
    Sun, Yi
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (03) : 2012 - 2045