Data-Driven method for assessing skill-opportunity recognition in open procedural problem solving environments

Authors:
Michael John Eagle;Tiffany Barnes
Affiliations:
The University of North Carolina at Charlotte, Charlotte, NC;The University of North Carolina at Charlotte, Charlotte, NC
Venue:
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Year:
2012

Citing 2
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Graphic interface design and deductive proof construction

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Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data

ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems

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Abstract

Our research goal is to use data-driven methods to generate the basic functionalities of intelligent tutoring systems. In open procedural problem solving environments, the tutor gives users a goal with little to no restrictions on how to reach it. Knowledge components refer to not only skill application, but also applicable skill-opportunity recognition. Syntax and logic errors further confound the results with ambiguity in error detection. In this work, we present a domain independent method of assessing skill-opportunity recognition. The results of this method can be used to provide automatic feedback to users as well as to assess users problem solving abilities.