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

By K. Koedinger, Albert T. Corbett, Charles A. Perfetti. Cognitive Sciences, 2012

775 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4d8e7c768bfcfb67401978767e9f696b8c048559

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Citations in the corpus

Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC (2015, neutral): “[8, 17], and classroom design research iterations [4, 19].”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “1 In this paper, we define the notion of skill as encompassing problem solving abilities, concept acquisition, simple memorization of factual information and, if the tutoring context allows, even motor skills—a related conceptualization is Koedinger et al’s (under review) notion of a “knowledge comp”
Towards a Theory of When and How Problem Solving Followed by Instruction Supports Learning (2017, neutral): “This outcome supports the notion that different learning goals (e.g., procedural fluency vs. conceptual understanding) require different instructional means (cf. Kalyuga and Singh 2015; Koedinger et al. 2012).”
Designing educational technologies in the age of AI: A learning sciences-driven approach (2019, background): “…instructional strategies such as optimal scheduling and feedback provision to provide foundational knowledge components, such as facts and rules, they are less effective at the provision of more complex instructions such as collaboration or self-explanations (Koedinger, Corbett, & Perfetti, 2012).”
How to make ‘more’ better? Principles for effective use of multiple representations to enhance students’ learning about fractions (2017, background): “Thus, visual fluency allows students to make direct one-on-one mappings between perceptual chunks and concepts (Koedinger et al. 2012).”
The Automation-by-Expertise-by-Training Interaction (2017, background): “Koedinger, Corbett, and Perfetti (2012) wrote that learning is robust when it lasts over time (long-term retention), transfers to new situations that differ from the learning situation along various dimensions (e.g., material content, setting, cf., Barnett & Ceci, 2002; Chen & Klahr, 2008), or…”
Handbook of Research on Learning and Instruction (2011, background): “Thus, children were taught to move based on elite, adult movement models rather than developmentally appropriate patterns such as those that Roberton (1982) described.”
Comparing Four Instructional Techniques for Promoting Robust Knowledge (2015, background): “…associated with induction and refinement processes), analogical comparison (primarily associated with understanding and sense-making processes), and prompting self-explanation (primarily associated with understanding and sense-making processes) (Koedinger et al. 2013; Koedinger et al. 2012).”