Data-Driven Method for Assessing Skill-Opportunity Recognition in Open Procedural Problem Solving Environments

  • Michael John Eagle
  • Tiffany Barnes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

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.

Keywords

Educational Data Mining Interaction Network 

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References

  1. 1.
    Barnes, T., Stamper, J.: Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 373–382. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1994)Google Scholar
  3. 3.
    Croy, M.J.: Graphic interface design and deductive proof construction. J. Comput. Math. Sci. Teach. 18, 371–385 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael John Eagle
    • 1
  • Tiffany Barnes
    • 1
  1. 1.The University of North Carolina at CharlotteCharlotteUSA

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