Academic journal article Australian Journal of Education

Connectionist Modelling and Education

Academic journal article Australian Journal of Education

Connectionist Modelling and Education

Article excerpt

Introduction

The main aim of this paper is to describe and explain some recent work on artificial neural networks (ANNs) that should be of interest to researchers in educational studies. These networks are also known more generally in the literature as connectionist systems. Although models of cognition would be of obvious relevance to those seeking to understand teaching and learning, a subsidiary aim of the paper will be to show that ANNs are relevant to the study of a wider range of educational phenomena.

The plan of the discussion is as follows. First, the development of ANN models is located within the context of the rise of what might usefully be called the `new cognitive science'. Then a taxonomy of the main models is provided, together with a fairly detailed account of the workings of one particular type. Three quite diverse applications are then discussed and their implications for education canvassed. Finally the significance of ANN models for representing both static and dynamical aspects of knowledge is touched upon. Since applications involve computer simulations, there will also be mention of a number of helpful software packages.

Traditional cognitive science

Fifty years ago, in a highly influential paper, the English mathematician Alan M. Turing (1950) sought to answer the question `Can machines think?' (p. 40). After transforming that question into one that was more manageable, he answered in the affirmative, speculating that `in about fifty years time' developments would be such that an interrogator would have only a 70 per cent chance of correctly distinguishing an (unseen) computer from a human after five minutes of questioning (p. 49). Despite the offer of a generous prize--the Loebner Prize, worth US$100,000, and a gold medal--and restrictions on the domain of questioning, no computer has been adjudicated as thinking since the annual contest commenced in 1991 (although adjudicators have classified a few humans as computers, Dennett, 1998, pp. 27-29).

Matters of prediction aside, Turing's lingering influence on traditional conceptualisations of cognition, or perhaps his capturing of a widely shared intuition, lay in the way he transformed the original question. His first strategy was epistemological and amounted to a behavioural test for thinking: the Turing Test. How could we ever tell if a machine was thinking? Answer: by asking it unrestricted questions. If it successfully imitates human answers--plays well the imitation game--then we may say it is thinking (Turing, 1950, pp. 40-43). His second strategy was ontological and involved a reconstrual of what we might regard as a machine. Since he was concerned, not with whether some particular machine could think, but with `whether there are imaginable computers which would do well [at imitation]' (p. 43), an approach was proposed that abstracted almost entirely from physical detail beyond requiring adequate (program) storage space and suitable speed of operation.

The upshot is that these two strategies conspire to yield a functionalist theory of mind, a very general set of constraints for determining the adequacy of accounts of cognition. The way it works is as follows. Roughly speaking, sets of inputs and outputs constitute the empirical evidence of cognition. Often winnowed selectively to comprise words, or other symbolic tokens, written or spoken, that elicit behaviours also often in the form of produced symbol tokens, cognitive acts are construed as the set of transformations that connect these inputs to their associated outputs. The best known general formulation of the class of intelligent cognitive acts is given by Newell and Simon (1976) in what they call `the Physical-Symbol System Hypothesis' which states that `a physical-symbol system has the necessary and sufficient means for general intelligent action' (p. 111). Construed as an empirical hypothesis, they are claiming essentially that the basis for intelligent action is to be found in the processes that operate on sets of symbols, or `symbol structures' to produce other symbol structures. …

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