• DocumentCode
    737223
  • Title

    Learnersourced Recommendations for Remediation

  • Author

    Li, Shang-Wen Daniel ; Mitros, Piotr

  • fYear
    2015
  • fDate
    6-9 July 2015
  • Firstpage
    411
  • Lastpage
    412
  • Abstract
    Rapid remediation of student misconceptions and knowledge gaps is one of the most effective ways to help students learn. We present a system for recommending additional resources, such as videos, reading materials, and web pages for students working through on-line course materials. This can provide remediations of knowledge gaps involving complex concepts. The system relies on learners suggesting resources which helped them, leveraging economies of scale as found in MOOCs and similar at-scale settings in order to build a rich body of remediations. The system allows for remediation of much deeper knowledge gaps than in prior work on remediation in MOOCs. We validated the system through a deployment in an introductory computer science MOOC. We found it lead to more in-depth remediation than prior strategies.
  • Keywords
    Artificial intelligence; Computer science; Crowdsourcing; Debugging; Education; Psychology; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on
  • Conference_Location
    Hualien, Taiwan
  • Type

    conf

  • DOI
    10.1109/ICALT.2015.72
  • Filename
    7265366