• DocumentCode
    3610734
  • Title

    Visual Analytics for MOOC Data

  • Author

    Qu, Huamin ; Chen, Qing

  • Author_Institution
    Hong Kong University of Science and Technology
  • Volume
    35
  • Issue
    6
  • fYear
    2015
  • Firstpage
    69
  • Lastpage
    75
  • Abstract
    With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.
  • Keywords
    Cryptography; Data mining; Data visualization; Distance education; Education courses; Online services; Visual analytics; MOOCs; clickstreams; computer graphics; visual analytics; visualization; web log data analysis;
  • fLanguage
    English
  • Journal_Title
    Computer Graphics and Applications, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1716
  • Type

    jour

  • DOI
    10.1109/MCG.2015.137
  • Filename
    7331178