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Custom Feedback Selection for Intelligent Tutoring Systems in Ill-Defined Domains

  • Author / Creator
    Johnson, Stuart H
  • Current medical imaging professional training uses an apprenticeship model with students following an established doctor and viewing their cases, in what is called a practicum. This posses an issue as students are limited to the cases available during their practicum. To resolve this automated instruction can aid in their education promoting both increased depth and breadth. To accom- plish this we have created a new Intelligent Tutoring System, Shufti. Shufti makes use of modern gamification and Intelligent Tutoring System designs to augment the learning experience of mammography students. In Shufti we have introduced a new reinforcement learning based technique for use in Intelligent Tutoring System feedback selection in ill-defined domains, and have made use of modern gamification techniques to increase learner engagement.

  • Subjects / Keywords
  • Graduation date
    2016-06:Fall 2016
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3B27Q02Z
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Zaiane, Osmar (Computing Science)
  • Examining committee members and their departments
    • Zaiane, Osmar (Computing Science)
    • Basu, Anup (Computing Science)
    • Rourke, Liam (Medicine)