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Permanent link (DOI): https://doi.org/10.7939/R3B27Q02Z

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

Descriptions

Other title
Subject/Keyword
Intelligent Tutoring System
ITS
Reinforcement Learning
RL
Machine Learning
ML
Feedback
Gamification
Ill-defined Domains
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Johnson, Stuart H
Supervisor and department
Zaiane, Osmar (Computing Science)
Examining committee member and department
Basu, Anup (Computing Science)
Rourke, Liam (Medicine)
Zaiane, Osmar (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2016-09-26T11:11:45Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3B27Q02Z
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
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