How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

The Science of Learning: A Systems Theory Approach provides authoritative, comprehensive, learner-centric reviews and discussions of theories and research on learning processes, instructional approaches, and the uses of instructional media. It includes over 600 references to the most influential theoretical and empirical literature in the field. It also provides discussions on the scientific method and how to apply science and scientific thinking to the study of learning, the development of instruction, and the evaluation of instructional programs. The systems-theory orientation provided in the book helps the reader understand the diverse data on learning and helps to integrate these data into a rich knowledge base. The book also summarizes guidance on the application of learning research to enhance learning effectiveness and illustrates this guidance with real-world examples.

The shared platform of the articles collected in this volume is usedto advocate a dynamical systems approach to cognition. It is arguedthat recent developments in cognitive science towards an account ofembodiment, together with the general approach of complexity theoryand dynamics, have a major impact on behavioral and cognitivescience.

Systems That Learn presents a mathematical framework for the study of learning in a variety of domains. It provides the basic concepts and techniques of learning theory as well as a comprehensive account of what is currently known about a variety of learning paradigms.Daniel N. Osherson and Scott Weinstein are at MIT, and Michael Stob at Calvin College.

"The book provides A guidelines approach on how to implement the proposed theory and tools in e-learning programs"--Provided by publisher.

Foundations of the Theory of Learning Systems