A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to explore the phenomena of off-task gaming behavior within the Assistments system, as well as to develop a passive visual indicator to deter and prevent off-task gaming behavior without active intervention via graphical feedback to the student and teachers. Traditional active intervention approaches were also constructed for comparison purposes, and machine-learned gaming-detection models were developed as a potential invocation and evaluation mechanism. Passive graphical interventions have been well received by teachers, and results are suggestive that they are effective at reducing off-task gaming behavior.

Last modified

• 2021-02-01

Creator

• Walonoski, Jason A

Contributors

• Heffernan, Neil

Degree

• MS

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-010806-205001

Keyword

• Machine Learning
• Behavior
• Intelligent tutoring systems
• Learning

Advisor

• Heffernan, Neil

Defense date

• 2005-12-02

Year

• 2006

Date created

• 2006-01-08

Resource type

• Thesis

Rights statement

• In Copyright

License

• All rights reserved