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An Introduction to Innovative Teaching and Learning

  • D. Tedman
  • L. C. Jain
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 36)

Abstract

This chapter presents an introduction to innovative teaching and learning and knowledge-based intelligent paradigms The intrinsic nature of knowledge-based intelligent techniques involves an accommodation with the pervasive imprecision of the real world, with the human mind as the role model [1]. Thus there are two important issues that should be considered in the design of effective teaching and learning strategies in this area. The first is the need for careful consideration of the discussions over the years by eminent researchers in regard to the epistemology and thinking processes involved in science and technology, as an appropriate starting point for the design of innovative teaching strategies for knowledge-based intelligent techniques. Secondly, since an aim of education in science and technology is to prepare students for their lives in societies which are increasingly dependent upon technology, reflection upon the nature of science and technology is of great benefit for the design of curricula and learning strategies in knowledge-based intelligent techniques.

Keywords

Artificial Neural Network Fuzzy Logic Online Teaching Scientific Revolution Evolutionary Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • D. Tedman
    • 1
  • L. C. Jain
    • 2
  1. 1.Flexible Learning CentreUniversity of South Australia AdelaideUnderdaleAustralia
  2. 2.Knowledge-Based Intelligent Engineering Systems CentreUniversity of South Australia AdelaideMawson LakesAustralia

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