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Artificial Neural Networks for Modeling Knowing and Learning in Science
Author(s) -
Roth WolffMichael
Publication year - 2000
Publication title -
journal of research in science teaching
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.067
H-Index - 131
eISSN - 1098-2736
pISSN - 0022-4308
DOI - 10.1002/(sici)1098-2736(200001)37:1<63::aid-tea5>3.0.co;2-h
Subject(s) - cognitive science , cognition , artificial neural network , context (archaeology) , psychology , artificial intelligence , computer science , neuroscience , biology , paleontology
Recent neurobiological evidence suggests that environmentally derived activity plays a central role in regulating neuronal growth and neuronal connectivity. Artificial neural networks with distributed representations display many features of knowing and learning that are known from biological intelligence. In this article, I advocate artificial neural networks as models for cognition and development. These models and how they work are exemplified in the context of a well‐known Piagetian developmental task and school science activity: balance beam problems. I conclude that artificial neural networks, because of their profoundly interactivist nature, are ideal tools for modeling cognitive development and learning in science. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 63–80, 2000