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Related Experiment Videos

Constraints on learning in nonprivileged domains

R S Siegler1, K Crowley

  • 1Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213.

Cognitive Psychology
|October 1, 1994
PubMed
Summary
This summary is machine-generated.

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Children can evaluate strategies in new learning domains before using them. Understanding goals helps constrain and evaluate strategies, even advanced ones, in nonprivileged learning areas.

Area of Science:

  • Cognitive Development
  • Learning Sciences
  • Developmental Psychology

Background:

  • Constraints on learning are not limited to evolutionarily privileged domains.
  • Understanding the goals of a task is crucial for constraining strategy generation and evaluation.
  • Children's conceptual understanding can influence their evaluation of strategies.

Purpose of the Study:

  • To investigate if constraints on learning operate in nonprivileged domains.
  • To examine the role of conceptual understanding in children's strategy evaluation.
  • To compare strategy evaluation in privileged and nonprivileged domains.

Main Methods:

  • Experiment 1: Assessed 5-year-olds' evaluation of addition strategies (min, illegitimate, counting from one).
  • Experiment 2: Assessed 9-year-olds' evaluation of tic-tac-toe strategies (forking, win/block).

Related Experiment Videos

  • Children evaluated strategies they did not yet use, including conceptually advanced ones.

    • Main Results:

    • Children accurately evaluated strategies based on their conceptual understanding, even novel or advanced ones.
    • 5-year-olds judged the min strategy as smarter than an illegitimate one and equal to their current strategy.
    • 9-year-olds judged the forking strategy as smarter than their win/block approach.

    Conclusions:

    • Conceptual understanding constrains strategy evaluation in nonprivileged domains, similar to privileged domains.
    • Children can assess the effectiveness of strategies before adopting them.
    • Commonalities and potential differences exist in learning constraints across domains.