Abstract

This dissertation consists of two articles. The first article describes an architecture for intelligent tutoring that focuses on modularity. This new architecture is based on Gibbons' layers theory for instructional design (2014). Splitting up the architecture for an intelligent tutor into layers allows different pieces to age at different rates which, in turn, allows the intelligent tutor to be adapted to new research and design theories. This architecture supports building intelligent tutoring services, nimble programs that can be assembled together to replicate the functions of intelligent tutoring without the expertise needed to create the services. Alternative architectures support building intelligent tutoring systems, monolithic programs that are less amenable to change and require immense expertise. The second article provides a proof of concept for the first services created under the layers theory. These two services create the building blocks of a domain and comprise one part of the content layer as described in the first article. The first service focuses on the task of key concept extraction whereas the second service focuses on prerequisite relationship extraction. These two tasks can provide the structure of the domain, particularly when it comes to domains that are more declarative in nature rather than procedural.

Degree

PhD

College and Department

David O. McKay School of Education; Instructional Psychology and Technology

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2020-04-07

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd11092

Keywords

intelligent tutoring, intelligent tutoring services, layers theory

Language

English

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