The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 36 (5):757-798 (2012)
Despite the accumulation of substantial cognitive science research relevant to 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. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for 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 (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices
|Keywords||Education Experimentation Instructional principles Learning principles Cognitive task analysis Cognitive modeling Knowledge representation|
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Citations of this work BETA
David W. Braithwaite, Robert L. Goldstone, Han L. J. van der Maas & David H. Landy (2016). Non-Formal Mechanisms in Mathematical Cognitive Development: The Case of Arithmetic. Cognition 149:40-55.
Alexander Renkl (2014). Toward an Instructionally Oriented Theory of Example‐Based Learning. Cognitive Science 38 (1):1-37.
Anna N. Rafferty, Emma Brunskill, Thomas L. Griffiths & Patrick Shafto (2015). Faster Teaching Via POMDP Planning. Cognitive Science 40 (1).
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