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Learning subgoals and methods for solving probability problems.

R Catrambone1, K J Holyoak

  • 1School of Psychology, Georgia Institute of Technology, Atlanta 30332.

Memory & Cognition
|November 1, 1990
PubMed
Summary
This summary is machine-generated.

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Learning quantitative problem-solving involves recognizing subgoals and methods. Examples highlighting specific subgoals or method conditions improve transfer learning, aiding in adapting skills to new problems.

Area of Science:

  • Cognitive Science
  • Educational Psychology
  • Artificial Intelligence

Background:

  • Learners often struggle with quantitative problem-solving transfer.
  • Example problems may implicitly teach problem-solving strategies.
  • The structure and features of examples influence learning.

Purpose of the Study:

  • To investigate if example problems can be represented by subgoals and methods.
  • To determine how example features affect subgoal recognition and method adaptation.
  • To predict transfer performance based on a subgoal/method representational scheme.

Main Methods:

  • Two experiments were conducted using quantitative example problems.
  • Participants studied examples designed to teach specific subgoals or methods.

Related Experiment Videos

  • Transfer problems were used to assess subgoal recognition and method adaptation.

    • Main Results:

    • Examples predicted to teach specific subgoals facilitated subgoal recognition in novel problems.
    • Demonstrating multiple methods did not enhance transfer compared to single methods.
    • Highlighting method application conditions improved learners' adaptation of methods to new problems.

    Conclusions:

    • A subgoal/method representational scheme may effectively predict learning and transfer.
    • The design of example problems significantly impacts learners' ability to generalize knowledge.
    • Explicitly teaching method conditions enhances adaptive expertise in quantitative domains.