Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Educational implications of analogy. A view from case-based reasoning

J L Kolodner1

  • 1College of Computing, Georgia Institute of Technology, Atlanta 30332-0280, USA. jlk@cc.gatech.edu

The American Psychologist
|January 1, 1997
PubMed
Summary

Case-based reasoning (CBR) uses analogy to solve problems and model cognition. Its computational approach offers insights into enhancing human learning and educational strategies.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

True hermaphroditism in a hog.

Journal of the American Veterinary Medical Association·1955
Same author

Anomalous genitalia in heifers.

The North American veterinarian·1948
See all related articles

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Educational Psychology

Background:

  • Case-based reasoning (CBR) is a problem-solving paradigm centered on analogy.
  • Its computational models aim to elucidate cognitive processes in reasoning.
  • CBR offers a framework for understanding human cognition and learning.

Purpose of the Study:

  • To explore the role of computational modeling in understanding analogical reasoning.
  • To derive hypotheses about human cognition through CBR.
  • To investigate the potential of CBR in enhancing cognitive abilities and educational applications.

Main Methods:

  • Utilizing computational modeling to represent case-based reasoning processes.
  • Analyzing the core components of analogical reasoning: encoding, retrieval, and adaptation.

Related Experiment Videos

  • Developing algorithms that mimic and potentially improve human cognitive functions.

    • Main Results:

    • CBR computational models effectively illustrate the mechanisms of encoding, retrieval, and adaptation in analogical reasoning.
    • The study provides insights into the algorithmic underpinnings of cognitive enhancement.
    • CBR is presented as a viable cognitive model with practical implications.

    Conclusions:

    • Case-based reasoning serves as a valuable computational model for understanding cognition.
    • CBR research can inform educational philosophy, practice, and software design.
    • The findings suggest pathways for enhancing human cognitive capabilities through AI-driven insights.