Modelling Changes in Understanding: Case Studies in Physical Reasoning(4 Advances in Learning and Instruction Series)

Psychologists, science educators and computer scientists team up 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. Focusing on the discipline of physics, this volume discusses the following topics: the difference in the organization of knowledge between experts and novices; the construction of accurate models of the learner at different stages in the knowledge acquisition process; and 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.).

Review :
Eileen Scanlon The main target audience is researchers in science education and cognitive scientists for whom physics learning has been a great source of interest. The relevance of the book to those interested in instructional design is that in the words of the editors, it contributes to an "understanding of the distance between students' conceptions and the knowledge to be taught". It is undoubtedly true that the data presented in the early chapters of the book are of interest. Where the general reader may have more difficulty is in appreciating the contribution of the machine learning approach to understanding human learning, In this Kurt Van Lehn's introductory remarks are helpful, pointing out the different experimental and modelling approaches used in different chapters of the book. However, a remark made in the editors' general overview states that the authors of the book have diverse opinions regarding this relationship between machine and human learning. If this topic interests you, there is a range of approaches on offer here. Computers & Education, Vol 31