Collaborative learning : cognitive and computational approaches
Author(s)
Bibliographic Information
Collaborative learning : cognitive and computational approaches
（Advances in learning and instruction series）
Pergamon, 1999
Available at / 22 libraries
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University of Tsukuba Library, Library on Library and Information Science
375.13-D7410003308470
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Note
Includes bibliographical references (p. 219-241) and index
Second impression published by Elsevier, 2003
Description and Table of Contents
Description
Research on collaborative learning is currently a very popular topic in education, psychology and computer science. In recent years, educational research has attempted to determine under what circumstances collaborative learning is more effective than learning alone, and more recently, numerous studies have focused on computer-mediated collaborative learning. In psychology, interest in collaborative learning is related to the emergence of new theories such as 'shared cognition' and 'distributed cognition'. These theories move away from the view traditionally held in cognitive science according to which human cognition is bound inside individual heads. The word 'collaboration' is also used very frequently in computer science to describe the interactions among artificial agents. The term has often been used rather loosely in the different research communities: this book is a contribution towards refining and operationalizing the concept.
Table of Contents
Acknowledgement. Contributors. Introduction: what do you mean by 'collaborative learning'? (P. Dillenbourg). Learning together: understanding the processes of computer-based collaborative learning (K. Littleton, P. Hakkinen). The role of grounding in collaborative learning tasks (M. Baker et al.). What is "multi" in multi-agent learning? (G. Weiss, P. Dillenbourg). Comparing human-human and robot-robot interactions (R. Joiner et al.). Learning by explaining to oneself and to others (R. Ploetzner et al.). Knowledge transformations in agents and interactions: a comparison of machine learning and dialogue operators (E. Mephu Nguifo et al.). Can analytic models support learning in groups? (H.U. Hoppe, R. Ploetzner). Using telematics for collaborative knowledge construction (T. Hansen et al.). The productive agency that drives collaborative learning (D. Schwatrtz). References. Index.
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