Learning with Multiple Representations

Aims to collect papers on learning declarative knowledge and problem solving skills that involve multiple representations such as graphical and mathematical representations, knowledge at different levels of abstraction. This book covers approaches to this topic from different perspectives: educational, cognitive modelling and machine learning.
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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