• DocumentCode
    3712425
  • Title

    Codepourri: Creating visual coding tutorials using a volunteer crowd of learners

  • Author

    Mitchell Gordon;Philip J. Guo

  • Author_Institution
    Department of Computer Science, University of Rochester, NY 14627, USA
  • fYear
    2015
  • Firstpage
    13
  • Lastpage
    21
  • Abstract
    A common way to learn is by studying written step-by-step tutorials such as worked examples. However, tutorials for computer programming can be tedious to create since a static text-based format cannot convey what happens as code executes. We created a system called Codepourri that enables people to easily create visual coding tutorials by annotating steps in an automatically-generated program visualization. Using Codepourri, we developed a novel crowdsourcing workflow where learners who are visiting an educational Web site (www. pythontutor.com) collectively create a tutorial by annotating execution steps in a piece of code and then voting on the best annotations. Since there are far more learners than experts, using learners as a crowd is a potentially more scalable way of creating tutorials. Our experiments with 4 expert judges and 101 learners adding 145 raw annotations to two pieces of textbook Python code show the learner crowd´s annotations to be accurate, informative, and containing some insights that even experts missed.
  • Keywords
    "Visualization","Tutorials","Manuals","Table lookup"
  • Publisher
    ieee
  • Conference_Titel
    Visual Languages and Human-Centric Computing (VL/HCC), 2015 IEEE Symposium on
  • Type

    conf

  • DOI
    10.1109/VLHCC.2015.7357193
  • Filename
    7357193