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The Knowledge-Learning-Instruction (KLI) Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning

Kenneth R. Koedinger, Albert T. Corbett, Charles Perfetti

learning-scienceinstructional-designknowledge-components

Abstract

The KLI framework proposes that optimal instructional choices change depending on content, and identifies three coordinated taxonomies across knowledge, learning, and instruction domains. It demonstrates how conflicting recommendations from learning science literature can be resolved through analysis of how instructional methods function to facilitate different learning mechanisms needed to achieve different knowledge acquisition goals.

Summary

The landmark 2012 paper from Carnegie Mellon’s LearnLab introduces a comprehensive framework for understanding how knowledge type, learning process, and instruction interact.

Core Framework Structure

KLI identifies three interconnected taxonomies:

  1. Knowledge Components (KCs): The specific cognitive units being learned - facts, concepts, procedures, schemas
  2. Learning Processes: The cognitive mechanisms through which KCs are acquired
  3. Instructional Methods: The teaching techniques that support specific learning processes

Three Categories of Learning Events

The framework identifies three primary learning process categories:

  1. Memory and fluency processes - Develop automaticity and recall through repetition and practice
  2. Induction and refinement processes - Build and adjust knowledge structures through examples and feedback
  3. Understanding and sense-making processes - Construct meaningful interpretations through explanation and elaboration

Key Insight: Matching Instruction to Knowledge Type

Different knowledge goals require different optimal configurations of instructional techniques because they require different primary learning processes.

For example, the framework resolves the apparent conflict between research supporting “more testing” versus “more worked examples” - the optimal choice depends on whether the target knowledge is procedural (benefiting from practice) or conceptual (benefiting from examples).

Empirical Foundation

The framework draws on over 400,000 hours of human learning data collected over ten years at LearnLab, with analyses spanning science, mathematics, and language learning domains.

Practical Application

KLI provides systematic methods for:

  • Analyzing what knowledge students need to acquire
  • Determining which learning processes support that knowledge type
  • Selecting instructional methods that facilitate those processes

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