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The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning

Paper · 2012 · Kenneth R. Koedinger, Albert T. Corbett and Charles Perfetti

Summary

Cognitive science has produced many well-tested theories of learning, yet educators still disagree about how to turn that research into classroom decisions. Part of the problem is scale: educational theories reason about large units such as groups and settings, while cognitive models such as ACT-R or neural networks describe very fine-grained mechanisms. Koedinger, Corbett and Perfetti propose the Knowledge-Learning-Instruction (KLI) framework, which works at an intermediate "Learning Event" level. KLI connects observable instructional and assessment activities to unobservable changes in a learner's knowledge. It organizes these connections through three coordinated taxonomies covering knowledge components (KCs), learning processes and instructional choices. The central claim is a chain of dependencies: the type of knowledge being acquired limits which learning processes apply, and those processes determine which teaching techniques best produce robust learning. Robust learning is learning that lasts, transfers and accelerates later learning. The authors illustrate this with examples from mathematics, science and language, many from in vivo experiments in real courses. KLI is offered as a generator of testable, domain-sensitive instructional hypotheses rather than as a finished theory.

Key points

  • The framework separates three kinds of events: instructional events (IEs), assessment events (AEs) and learning events (LEs). IEs and AEs are observable, while LEs are inferred.
  • LEs fall into three broad classes:
    • memory and fluency processes;
    • induction and refinement processes;
    • understanding and sense-making processes.
  • KCs are classified by mapping type: constant-constant (facts and paired associates), variable-constant (categories) and variable-variable (general rules and principles).
  • Optimal instruction depends on KC type:
    • Gradually fading worked examples into problems suits variable-variable KCs that have rationales.
    • For constant-constant KCs, showing study examples only after failed test attempts may work better.
  • The research comes from the Pittsburgh Science of Learning Center (learnlab.org), funded by NSF award SBE-0836012. It mostly involves junior-high to college students. As a rule of thumb, a KC should be split into smaller KCs when incoming student success falls well below roughly 95%.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States633 works · 195,218 citations🎓University of PittsburghPittsburgh · United States68 works · 25,442 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Psychology · 1,673 worksExperimental and Cognitive Psychology · 950 worksDevelopmental and Educational Psychology · 533 worksNeuroscience · 192 worksCognitive Neuroscience · 191 works

Cite

Cite as Kenneth R. Koedinger, Albert T. Corbett and Charles Perfetti. 2012. The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning. Cognitive Science 36(5).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1111/j.1551-6709.2012.01245.x

@inproceedings{koedinger2012knowledge,
  title = {{The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning}},
  author = {Kenneth R. Koedinger, Albert T. Corbett and Charles Perfetti},
  booktitle = {Cognitive Science 36(5)},
  year = {2012},
  doi = {10.1111/j.1551-6709.2012.01245.x},
  url = {https://doi.org/10.1111/j.1551-6709.2012.01245.x}
}

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