Learning with Multiple Representations

When knowledge in a domain serves different tasks, such as diagnostic problem solving in medicine and explaining/communicating this knowledge to others, knowledge representation and handling pose difficult problems. This book provides answers to questions of multiple representations in human reasoning and learning. Computational approaches to learning with multiple representations are introduced and discussed. Finally, the role of multiple representations in teaching is addressed. Learning with Multiple Representations is one of the outcomes of the European Science Foundation's research programme on Learning in Humans and Machines, and will be of interest to a wide audience of psychologists, computer scientists and educational scientists.

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