The book is written in the framework of a European collaborative research programme, Learning in Humans and Machines, funded by the European Science Foundation. The book's purpose is to collect papers on learning declarative knowledge and problem solving skills that involve multiple representations such as verbal, graphical and mathematical representations, knowledge at different levels of abstraction (e.g. qualitative and quantitative, specific cases or general models). One of the goals of the research programme is to demonstrate existing and to initiate new collaboration between educational sciences, psychology and machine learning.
The first aim of this book is to give an overview of the state of the art of the topic of learning involving different representations in the form of overview chapters. The book covers approaches to this topic from different perspectives: educational, cognitive modelling and machine learning. It includes both theoretical analyses and studies in application contexts. The second aim of the book is to present current research on these topics and to articulate research issues for future research. This is done in the form of a collection of research papers and two reflective chapters.
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Arthur B. Markman, Department of Psychology, University of Texas
...the volume explores a wide range of important topics that deal with multiple representations, and thus provides a nice introduction to research in this area. This strength must be emphasized, as the coordination of multiple representations is topic that has not received as much attention in cognitive science as it deserves....the book will be of value for researchers who are already well-versed in general issues of representation and are interested in the state of the art in research on the role of multiple representations in education and expert performance....it is clear that expert performance requires the coordination of multiple representations and that the education of future expert performance requires the coordination of multiple representations and that the education of future experts will require understanding of how to teach people to carry out this coordination. There is clearly a gap that must be bridged between current educational systems, in which the use of multiple external representations may actually hamper learning, and expert performance, which is characterised by the ability to shift among different internal representations of a situation. This book will be a valuable resource for psychologists (and educational psychologists) who are interested in these issues.
British Journal of Educational Psychology, Vol 70, Part 1
The book is written in the framework of a European collaborative research programme, Learning in Humans and Machines, funded by the European Science Foundation. The book's purpose is to collect papers on learning declarative knowledge and problem solving skills that involve multiple representations such as verbal, graphical and mathematical representations, knowledge at different levels of abstraction (e.g. qualitative and quantitative, specific cases or general models). One of the goals of the research programme is to demonstrate existing and to initiate new collaboration between educational sciences, psychology and machine learning.
The first aim of this book is to give an overview of the state of the art of the topic of learning involving different representations in the form of overview chapters. The book covers approaches to this topic from different perspectives: educational, cognitive modelling and machine learning. It includes both theoretical analyses and studies in application contexts. The second aim of the book is to present current research on these topics and to articulate research issues for future research. This is done in the form of a collection of research papers and two reflective chapters.
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