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Why Tutored Problem Solving May be Better Than Example Study: Theoretical Implications from a Simulated-Student Study

  • Noboru Matsuda
  • William W. Cohen
  • Jonathan Sewall
  • Gustavo Lacerda
  • Kenneth R. Koedinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5091)

Abstract

Is learning by solving problems better than learning from worked-out examples? Using a machine-learning program that learns cognitive skills from examples, we have conducted a study to compare three learning strategies: learning by solving problems with feedback and hints from a tutor, learning by generalizing worked-out examples exhaustively, and learning by generalizing worked-out examples only for the skills that need to be generalized. The results showed that learning by tutored problem solving outperformed other learning strategies. The advantage of tutored problem solving was mostly due to the error detection and correction that was available only when skills were applied incorrectly. The current study also suggested that learning certain kinds of conditions to apply rules only for appropriate situations is quite difficult.

Keywords

Intelligent Authoring System Simulated Student Programming by Demonstration Machine Learning Cognitive Tutor 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Noboru Matsuda
    • 1
  • William W. Cohen
    • 2
  • Jonathan Sewall
    • 1
  • Gustavo Lacerda
    • 2
  • Kenneth R. Koedinger
    • 1
  1. 1.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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