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

  • Machine Learning
  • RL
  • Ill-defined Domains
  • Intelligent Tutoring System
  • Reinforcement Learning
  • ML
  • Gamification
  • ITS
  • Feedback

• Graduation date

  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.