|Citation||Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2011). The Knowledge-Learning-Instruction (KLI) Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognition.||Sidewiki|
Despite the accumulation of substantial cognitive science research relevant for education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. Expressed at a just-right medium grain size, KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints and opportunities of detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events: a) memory and fluency processes, b) induction and refinement processes, c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices.
One-liner: Defines a theoretical framework for knowledge, learning, and instruction including taxonomies for instructional, learning, and assessment events that occur during the processes of teaching and learning.
Basic unit of learning is knowledge component, e.g.
unit task level of Newells time scales of human action
Instruction makes KC application easier over time
KCs are hierarchical
Categorization by application condition and response condition; approximates complexity
See tables for more info and examples
These may be better understood by applying the KC taxonomy
“…instructional principles should refer to KCs rather than to domains”, e.g. use pure practice in math learning when learning algebra “grammar” rules p. 29
More on insturctional principles:
Induction and refinement:
Understanding and sense-making:
Asymmetry hypothesis: all three learning processes are useful for complex material; memory and fluency more important for simple material. In other words, failures to learn are sometimes failures in memory (Frank & Gibson, 2011). p. 37
“Expertise reversal effect”: Straight problem solving may be better for more experienced students (Kalyuga et al., 2003). KLI: Beginning students are doing KC induction and sense-making; advanced students, refinement and fluency building. p. 49