Evolution of an Intelligent Deductive Logic Tutor Using Data-Driven Elements

Article

DOI: 10.1007/s40593-016-0112-1

Cite this article as:
Mostafavi, B. & Barnes, T. Int J Artif Intell Educ (2017) 27: 5. doi:10.1007/s40593-016-0112-1

Abstract

Deductive logic is essential to a complete understanding of computer science concepts, and is thus fundamental to computer science education. Intelligent tutoring systems with individualized instruction have been shown to increase learning gains. We seek to improve the way deductive logic is taught in computer science by developing an intelligent, data-driven logic tutor. We have augmented Deep Thought, an existing computer-based logic tutor, by adding data-driven methods, specifically; intelligent problem selection based on the student’s current proficiency, automatically generated on-demand hints, and determination of student problem solving strategies based on clustering previous students. As a result, student tutor completion (the amount of the tutor the students completed) steadily improved as data-driven methods were added to Deep Thought, allowing students to be exposed to more logic concepts. We also gained additional insights into the effects of different course work and teaching methods on tutor effectiveness.

Keywords

Deductive logic instruction Intelligent tutoring systems Data-driven methods 

Funding information

Funder NameGrant NumberFunding Note
Center for Hierarchical Manufacturing, National Science Foundation
  • 1432156
National Science Foundation
  • 0845997

Copyright information

© International Artificial Intelligence in Education Society 2016

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA

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