Inferred Functions of Performance and Learningby Siegfried Engelmann, Donald Steely
Pub. Date: 10/28/2003
Publisher: Taylor & Francis
Engelmann (U. of Oregon) and Steely (Oregon Center for Applied Science) examine what the intelligent system that produces responses must do to perform as it does. Coverage includes the performance system that does not learn, basic learning, complicated learning, and human learning and how it is related to that of other organisms. Through a series of meta-blueprints articulating the steps, content or specific information, and logical operations required for the system to perform the specified tasks, the authors show how it would be possible to design machines that perform and learn in the same way as organisms. For practitioners involved in analyzing and creating behavior, such as ethnologists, instructional designers, learning psychologists, physiologist-neurobiologists, and designers of intelligent machines. Annotation ©2004 Book News, Inc., Portland, OR
Table of ContentsContents: Preface. Part I: Performance of Nonlearning Systems. A Framework for the Fundamentals of Performance. Basics of Hardwired Systems. Agent Functions. Interaction of Agent and Infrasystem. Part II: Basic Learning. Perspectives on Basic Learning. Basic Antecedent Learning. Basic Response-Strategy Learning. Learning Patterns and Generalizations. Transformation of Data. Part III: Extended Learning. Individuals and Features. Secondary and Unfamiliar Learning. Experimental Designs. Volition and Thought. Part IV: Human Learning and Instruction. Human Learning. Language. Human Cognitive Development. The Logic of Instruction. Issues.
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