Learning in Humans and Machines: Towards an Interdisciplinary Learning Science

Hardcover | December 21, 1995

EditorP. Reimann, H. SpadabyReimann

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The book discusses the analysis, comparison and integration of computational approaches to learning and research on human learning. Learning has for some time been an issue of minor importance in the cognitive sciences. It has, however, now become one of the most active research fields in psychology, the neurosciences, and computer science (machine learning).
The aim of this book is to provide the reader with an overview of the prolific research on learning throughout the disciplines.


The book will not only provide a general overview for those who are new to the field but will also provide specialist knowledge for those who want to learn more about alternative approaches and conceptualizations of learning in other disciplines. The contributing authors are all considered as leading experts in their field and come from the fields of cognitive, computer and educational science. They provide an assessment of the state-of-the-art of research, links between the disciplines, and they highlight the critically important research issues and methodologies, thus providing a basis for future research.

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The book discusses the analysis, comparison and integration of computational approaches to learning and research on human learning. Learning has for some time been an issue of minor importance in the cognitive sciences. It has, however, now become one of the most active research fields in psychology, the neurosciences, and computer sci...

Format:HardcoverDimensions:324 pages, 9.41 × 7.24 × 0.69 inPublished:December 21, 1995Publisher:Pergamon

The following ISBNs are associated with this title:

ISBN - 10:0080425690

ISBN - 13:9780080425696

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Table of Contents

Chapter headings: Towards an Interdisciplinary Learning Science (P. Reimann, H. Spada). A Cognitive Psychological Approach to Learning (S. Vosniadou). Learning to Do and Learning to Understand: A Lesson and a Challenge for Cognitive Modeling (S. Ohlsson). Machine Learning: Case Studies of an Interdisciplinary Approach (W. Emde). Mental and Physical Artifacts in Cognitive Practices (R. Säljö). Learning Theory and Instructional Science (E. De Corte). Knowledge Representation Changes in Humans and Machines (L. Saitta and Task Force 1). Multi-Objective Learning with Multiple Representations (M. Van Someren, P. Reimann). Order Effects in Incremental Learning (P. Langley). Situated Learning and Transfer (H. Gruber et al.). The Evolution of Research on Collaborative Learning (P. Dillenbourg et al.). A Developmental Case Study on Sequential Learning: The Day-Night Cycle (K. Morik, S. Vosniadou). Subject index. Author index.

Editorial Reviews

Ephraim Nissan, University of GreenwichThe title of this book accurately describes its editors' ambition: outstretching both arms wide open to get hold of as diverse foci as learning in humans, versus what the discipline of machine learning (ML) within artificial intelligence (AI) actually amounts to in the main....Used properly...this volume can be a trove. A trove of leads to lead you outside the grasp of its compass. To the extent that the book can do that for the reader, it has fulfilled its purpose. No other single book, to my knowledge, would do the same for us on this global subject.Pragmatics & CognitionA certain unity (in this publication's) approach, focusing on the analysis of phenomena in their compexity and developing a "flexible" vision of learning, integrating the role of context, goals and previous knowledge, gives an undeniable coherence to this work.L'Annee Psychologique