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  1.  32
    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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  2.  57
    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.
    Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving (...)
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  3. 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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  4.  61
    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.
    This article explores the complementary strengths and weaknesses of grounded and abstract representations in the domain of early algebra. Abstract representations, such as algebraic symbols, are concise and easy to manipulate but are distanced from any physical referents. Grounded representations, such as verbal descriptions of situations, are more concrete and familiar, and they are more similar to physical objects and everyday experience. The complementary computational characteristics of grounded and abstract representations lead to trade‐offs in problem‐solving performance. In prior research with (...)
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  5.  25
    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.
  6.  82
    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.
  7.  35
    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.
    This study investigated the cognitive processes involved in inductive reasoning. Sixteen undergraduates solved quadratic function–finding problems and provided concurrent verbal protocols. Three fundamental areas of inductive activity were identified: Data Gathering, Pattern Finding, and Hypothesis Generation. These activities are evident in three different strategies that they used to successfully find functions. In all three strategies, Pattern Finding played a critical role not previously identified in the literature. In the most common strategy, called the Pursuit strategy, participants created new quantities from (...)
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  8. 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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  9.  43
    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.
  10.  40
    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.  10
    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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  12.  77
    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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  13. 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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  14. 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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  15.  37
    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.  34
    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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  17. 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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  18. 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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