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Kenneth Richard Koedinger
Carnegie Mellon University
  1. The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning.Kenneth R. Koedinger, Albert T. Corbett & Charles Perfetti - 2012 - Cognitive Science 36 (5):757-798.
    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 (...)
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  2.  22
    Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry.Kenneth R. Koedinger & John R. Anderson - 1990 - Cognitive Science 14 (4):511-550.
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  3.  26
    An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor.Vincent A. W. M. M. Aleven & Kenneth R. Koedinger - 2002 - Cognitive Science 26 (2):147-179.
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  4.  46
    Trade‐Offs Between Grounded and Abstract Representations: Evidence From Algebra Problem Solving.Kenneth R. Koedinger, Martha W. Alibali & Mitchell J. Nathan - 2008 - Cognitive Science 32 (2):366-397.
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  5.  72
    LearnLab's DataShop: A Data Repository and Analytics Tool Set for Cognitive Science.Kenneth R. Koedinger, John C. Stamper, Brett Leber & Alida Skogsholm - 2013 - Topics in Cognitive Science 5 (3):668-669.
  6.  12
    Is it better to give than to receive? The assistance dilemma as a fundamental unsolved problem in the cognitive science of learning and instruction.Kenneth R. Koedinger, Phillip Pavlik, Bruce M. McLaren & Vincent Aleven - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  7. Seeing language learning inside the math: Cognitive analysis yields transfer.Kenneth R. Koedinger & Elizabeth A. McLaughlin - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 471--476.
     
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  8.  34
    Is self-explanation always better? the effects of adding self-explanation prompts to an english grammar tutor.Ruth Wylie, Kenneth R. Koedinger & Teruko Mitamura - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1300--1305.
  9.  23
    Solving Inductive Reasoning Problems in Mathematics: Not‐so‐Trivial Pursuit.Lisa A. Haverty, Kenneth R. Koedinger, David Klahr & Martha W. Alibali - 2000 - Cognitive Science 24 (2):249-298.
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  10.  23
    Testing Theories of Transfer Using Error Rate Learning Curves.Kenneth R. Koedinger, Michael V. Yudelson & Philip I. Pavlik - 2016 - Topics in Cognitive Science 8 (3):589-609.
    We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide a common (...)
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  11. Helping students know'further'-increasing the flexibility of students' knowledge using symbolic invention tasks.Ido Roll, Vincent Aleven & Kenneth R. Koedinger - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1169--74.
     
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  12. Key misconceptions in algebraic problem solving.Julie L. Booth & Kenneth R. Koedinger - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 571--576.
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  13. Regular articles Perceiving temporal regularity in music* 1 Edward W. Large, Caroline Palmer Memory for goals: an activation-based model* 39 Erik M. Altmann, J. Gregory Trafton. [REVIEW]John R. Anderson, Deb K. Roy, Alex P. Pentland, Vincent Awmm Aleven, Kenneth R. Koedinger, Yafen Lo, Ashley Sides, Joseph Rozelle, Daniel Osherson & Bruno Laeng - 2002 - Cognitive Science 26 (837):839.
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  14.  1
    Integrating representation learning and skill learning in a human-like intelligent agent.Nan Li, Noboru Matsuda, William W. Cohen & Kenneth R. Koedinger - 2015 - Artificial Intelligence 219 (C):67-91.
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  15.  30
    When and how often should worked examples be given to students? New results and a summary of the current state of research.Bruce M. McLaren, Sung-Joo Lim & Kenneth R. Koedinger - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 2176--2181.
  16. The effect of prior conceptual knowledge on procedural performance and learning in algebra.Julie L. Booth, Kenneth R. Koedinger & Robert S. Siegler - 2007 - In McNamara D. S. & Trafton J. G. (eds.), Proceedings of the 29th Annual Cognitive Science Society. Cognitive Science Society. pp. 137--142.
     
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  17.  48
    The developmental progression from implicit to explicit knowledge: A computational approach.Martha Wagner Alibali & Kenneth R. Koedinger - 1999 - Behavioral and Brain Sciences 22 (5):755-756.
    Dienes & Perner (D&P) argue that nondeclarative knowledge can take multiple forms. We provide empirical support for this from two related lines of research about the development of mathematical reasoning. We then describe how different forms of procedural and declarative knowledge can be effectively modeled in Anderson's ACT-R theory, contrasting this computational approach with D&P's logical approach. The computational approach suggests that the commonly observed developmental progression from more implicit to more explicit knowledge can be viewed as a consequence of (...)
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  18.  25
    Goals and Learning in Microworlds.Craig S. Miller, Jill Fain Lehman & Kenneth R. Koedinger - 1999 - Cognitive Science 23 (3):305-336.
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