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  1. Electrifying diagrams for learning: principles for complex representational systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
    Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, (...)
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  • Electrifying diagrams for learning: principles for complex representational systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
    Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, (...)
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  • Augmenting Cognitive Architectures to Support Diagrammatic Imagination.Balakrishnan Chandrasekaran, Bonny Banerjee, Unmesh Kurup & Omkar Lele - 2011 - Topics in Cognitive Science 3 (4):760-777.
    Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive (...)
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  • Cognitive processing of spatial relations in Euclidean diagrams.Yacin Hamami, Milan N. A. van der Kuil, Ineke J. M. van der Ham & John Mumma - 2020 - Acta Psychologica 205:1--10.
    The cognitive processing of spatial relations in Euclidean diagrams is central to the diagram-based geometric practice of Euclid's Elements. In this study, we investigate this processing through two dichotomies among spatial relations—metric vs topological and exact vs co-exact—introduced by Manders in his seminal epistemological analysis of Euclid's geometric practice. To this end, we carried out a two-part experiment where participants were asked to judge spatial relations in Euclidean diagrams in a visual half field task design. In the first part, we (...)
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  • Integrating visual and verbal knowledge during classroom learning with computer tutors.Kirsten R. Butcher & Vincent Aleven - 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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  • Diagram interaction during intelligent tutoring in geometry: Support for knowledge retention and deep understanding.Kirsten R. Butcher & 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. pp. 1736--1741.
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  • A fresh look at research strategies in computational cognitive science: The case of enculturated mathematical problem solving.Regina E. Fabry & Markus Pantsar - 2019 - Synthese 198 (4):3221-3263.
    Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from (...)
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  • Towards a new epistemology of mathematics.Bernd Buldt, Benedikt Löwe & Thomas Müller - 2008 - Erkenntnis 68 (3):309 - 329.
    In this introduction we discuss the motivation behind the workshop “Towards a New Epistemology of Mathematics” of which this special issue constitutes the proceedings. We elaborate on historical and empirical aspects of the desired new epistemology, connect it to the public image of mathematics, and give a summary and an introduction to the contributions to this issue.
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  • A comparison of information processing and dynamical systems perspectives on problem solving.Stephen K. Reed & Robin R. Vallacher - 2019 - Thinking and Reasoning 26 (2):254-290.
    This article compares the information processing and dynamical systems perspectives on problem solving. Key theoretical constructs of the information-processing perspective include “searching” a “p...
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  • Non-formal mechanisms in mathematical cognitive development: The case of arithmetic.David W. Braithwaite, Robert L. Goldstone, Han L. J. van der Maas & David H. Landy - 2016 - Cognition 149 (C):40-55.
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  • Peer feedback provision and mathematical proofs: Role of domain knowledge, beliefs, perceptions, epistemic emotions, and peer feedback content.Maryam Alqassab - 2017 - Dissertation, Ludwig Maximilians Universität, München
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  • 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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  • Strategy shifts and expertise in solving transformation rule problems.Vanessa J. Clarke Koen Lamberts - 1997 - Thinking and Reasoning 3 (4):271 – 290.
    The acquisition of expertise in formal problem solving has been assumed to involve either a shift from backwards to forwards inference, or a shift from unguided to guided forwards inference. In a longitudinal study, the acquisition of formal problem-solving expertise was investigated. Participants were tested as novices before undertaking controlled practice in the problem domain which involved transformation rule problems , and were finally tested as experts. The direction of inference in problem solutions was found to be inadequate to describe (...)
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  • Counterexample Search in Diagram‐Based Geometric Reasoning.Yacin Hamami, John Mumma & Marie Amalric - 2021 - Cognitive Science 45 (4):e12959.
    Topological relations such as inside, outside, or intersection are ubiquitous to our spatial thinking. Here, we examined how people reason deductively with topological relations between points, lines, and circles in geometric diagrams. We hypothesized in particular that a counterexample search generally underlies this type of reasoning. We first verified that educated adults without specific math training were able to produce correct diagrammatic representations contained in the premisses of an inference. Our first experiment then revealed that subjects who correctly judged an (...)
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  • (1 other version)The Role of Functionality in the Mental Representations of Engineering Students: Some Differences in the Early Stages of Expertise.Jarrod Moss, Kenneth Kotovsky & Jonathan Cagan - 2006 - Cognitive Science 30 (1):65-93.
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  • On automating diagrammatic proofs of arithmetic arguments.Mateja Jamnik, Alan Bundy & Ian Green - 1999 - Journal of Logic, Language and Information 8 (3):297-321.
    Theorems in automated theorem proving are usually proved by formal logical proofs. However, there is a subset of problems which humans can prove by the use of geometric operations on diagrams, so called diagrammatic proofs. Insight is often more clearly perceived in these proofs than in the corresponding algebraic proofs; they capture an intuitive notion of truthfulness that humans find easy to see and understand. We are investigating and automating such diagrammatic reasoning about mathematical theorems. Concrete, rather than general diagrams (...)
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  • CaMeRa: A computational model of multiple representations.Hermina J. M. Tabachneck-Schijf, Anthony M. Leonardo & Herbert A. Simon - 1997 - Cognitive Science 21 (3):305-350.
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  • Conservation principles and action schemes in the synthesis of geometric concepts.Luis A. Pineda - 2007 - Artificial Intelligence 171 (4):197-238.
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  • Prolegomena to a cognitive investigation of Euclidean diagrammatic reasoning.Yacin Hamami & John Mumma - 2013 - Journal of Logic, Language and Information 22 (4):421-448.
    Euclidean diagrammatic reasoning refers to the diagrammatic inferential practice that originated in the geometrical proofs of Euclid’s Elements. A seminal philosophical analysis of this practice by Manders (‘The Euclidean diagram’, 2008) has revealed that a systematic method of reasoning underlies the use of diagrams in Euclid’s proofs, leading in turn to a logical analysis aiming to capture this method formally via proof systems. The central premise of this paper is that our understanding of Euclidean diagrammatic reasoning can be fruitfully advanced (...)
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  • 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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  • Expert interpretation of bar and line graphs: the role of graphicacy in reducing the effect of graph format.David Peebles & Nadia Ali - 2015 - Frontiers in Psychology 6.
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  • Knowledge-based proof planning.Erica Melis & Jörg Siekmann - 1999 - Artificial Intelligence 115 (1):65-105.
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  • A pattern-recognition theory of search in expert problem solving.Fernand Gobet - 1997 - Thinking and Reasoning 3 (4):291 – 313.
    Understanding how look-ahead search and pattern recognition interact is one of the important research questions in the study of expert problem solving. This paper examines the implications of the template theory Gobet & Simon, 1996a , a recent theory of expert memory, on the theory of problem solving in chess. Templates are chunks Chase & Simon, 1973 that have evolved into more complex data structures and that possess slots allowing values to be encoded rapidly. Templates may facilitate search in three (...)
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  • 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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