Modelling changes in understanding : case studies in physical reasoning

Author(s)

    • Kayser, Daniel
    • Vosniadou, Stella

Bibliographic Information

Modelling changes in understanding : case studies in physical reasoning

edited by Daniel Kayser and Stella Vosniadou

(Advances in learning and instruction series)

Pergamon, 1999

  • : hc

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Note

Includes bibliographical references(p. 280-295) and index

Description and Table of Contents

Description

Published as part of the "Advances in Learning and Instruction" series, "Modelling Changes in Understanding" brings together Psychologists, Science Educators and Computer Scientists to describe and explain how we develop an understanding of the physical world around us. The contributors examine how changes in our understanding and consequent learning can be modelled using computer software. Focussing on the discipline of Physics, this volume discusses the following topics: the difference in the organisation of knowledge between experts and novices; the construction of accurate models of the learner at different stages in the knowledge acquisition process; computer models which claim to answer questions and accumulate understanding in a similar way to human beings.

Table of Contents

Acknowledgment. Contributors. Preface. General Overview (D. Kayser et al.). Introductory Remarks: three phases in the development of a new paradigm (K. VanLehn). Changes in categorization as a function of expertise and context in elementary mechanics (M.T. Bajo et al.). Conceptualising mental representations of mechanics: a method to investigate representational change (G.C. van der Veer et al.). Modelling elementary school students' solution of mechanics problems (S. Vosniadou et al.). Conceptual change as a logical theory revision process: a machine learning perspective (F. Esposito et al.). Conceptual change in the explanations of phenomena in astronomy (K. Morik, M. Muhlenbrock). Modelling conceptual change: representational issues (F. Neri et al.). Teaching the energy concept with the machine learning system APT (C. Nedellec, A. Tiberghien). Using dialogue analysis to capture teachers/student interactions that promote changes in understanding (G. Sabah et al.).

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