Abstract
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.
Highlights
The need for a learning-to-instruction theoretical frameworkThe cognitive and learning sciences have a substantial base of highly refined and extensively tested theories of cognition and learning (e.g., Anderson & Lebiere, 1998; McClelland, Cleeremans, & Servan-Schreiber, 1990; Newell, 1990; Sun, 1994)
While knowledge may well be non-symbolic in its brain-based implementation, for purposes of scientific analysis, we describe knowledge components in a symbolic format, but distinguish whether the KC represents an implicit association, skill, or procedure that a student cannot verbalize and an explicit concept or procedure that a student can verbalize
Our broad goal for this paper was to put forward a theoretical framework to organize the development of instructional theory at a grain size appropriate for guiding the design, development, and continual improvement of effective and efficient academic course materials, technologies, and instructor practices
Summary
The volume of research on learning and instruction is enormous. Yet progress in improving educational outcomes has been slow at best. An ideal scientific solution would be a small set of universal instructional principles that can be applied to produce efficient and robust student learning for any educational goal This holy grail is likely unattainable both because effective instructional practices in one subject-matter domain, like science, are often not effective in another, like second language and because even within a domain, specific instructional goals and contexts add restrictions to the application of principles. Its analysis of learning in terms of multi-level knowledge components reveals complexities that allow generalizations across domains These generalizations, in turn, support instructional principles of high generality that, when combined with instructional goals, allow practical suggestions about curricula and intervention decisions
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