Publication Date

2019

Document Type

Dissertation/Thesis

First Advisor

Freedman, Reva

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

Abstract

I used reinforcement learning to investigate which categories of hints are most efficient in an intelligent tutoring system for human anatomy. Efficiency is defined as minimizing the time it takes the student to learn the material. When a student gives a wrong answer, the tutor can give them a text hint, a diagrammatic hint, or a video clip. Each type of hint takes a different amount of time to deliver and takes the student a different amount of time to understand.

I built a simulator for the intelligent tutoring system to collect data from simulated students. I implemented reinforcement learning, in particular two Temporal Difference (TD) Learning techniques on this simulated data to identify the most efficient hint specific to a student and the most efficient hint for the whole student population. I show that the most efficient hint type is a function of the two times listed above.

Extent

53 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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