Before moving on to consider what might be meant by terms such as ‘scientifi c knowledge’ (in the next chapter), it is useful to consider how the approach adopted in this book might apply to realistic contexts. The model used here is a general one, with the cognitive system sensing and acting in ‘an environment’ without any further characterisation. This is only useful if it is applicable to realistic teaching and learning contexts. In science education, such environments are likely to include science classrooms with students (hopefully) listening to teachers, working in groups, talking to each other and/or a teacher, handling apparatus and materials and using texts and other learning resources. Other environments where student learning might take place would include fi eldwork, home study, informal learning from museums, leisure reading, viewing television programmes and so forth.
In these various contexts, the individual’s learning will depend upon particular features of the environment (the teacher, a peer, the Internet, etc.), as well as upon the internal cognitive resources within their nervous systems. The leaner is able, in these contexts, to draw upon additional (external) resources for thinking to use
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alongside their internal cognitive resources. This does not require a fundamental modifi cation of the model being developed in this book. Back in Part II (see Fig.
5.1 ), it was suggested that sensory information and memory provided two sources for thinking, and sensory information may be derived from teacher talk, discussions with peers, reading books, watching a practical demonstration and so forth.
In the introduction to the present part (Chap. 8 ), the general context of human cognition was set out in terms of sensing, modelling and acting upon the envi- ronment, then sensing the new state of the environment, providing feedback for modifying the internal model of, and guiding intelligent action in, the world (see Fig. 8.1 ). However, it is clear that the environment cannot be considered as a static and inert context in and on which an individual learner operates, and this raises the issue of whether resources in the environment should be considered, like cognitive resources, as a form of ‘knowledge’. This would be the perspective taken in some connectivist accounts of learning, where knowledge is considered to be distributed across networks – and these are not seen as limited to neural networks within a single individual – so that ‘knowledge may reside in non-human appliances’
(Strong & Hutchins, 2009 , p. 55).
In the approach taken in this book, the focus has been very much on the individual understood as a cognitive system. This is represented in Fig. 9.4 , which shows an individual who is interacting with both an object in the environment and another processor. The object could be a textbook, or some laboratory apparatus, for example, and the individual is able to both sense and act on the object. The processor can be understood as something more than a static object that can be manipulated but a special type of object able to actively process information: this could be a computer, for example, or, indeed, another person.
The same scenario is represented somewhat differently in Fig. 9.5 . Here the cognitive system is seen to include not only the individual learner but also those features of the environment supporting cognition. Cognition is distributed because processing is ‘shared’ between more than one processor. Just as each processor will have access to internal resources (the individual’s ‘memories’, a computer’s database), the object is also used as a resource. If knowledge is understood in terms of resources that can facilitate processing, then in this system, the knowledge is distributed across both processors and the object. So, in this perspective, knowledge resides in people and in computers, and in textbooks, and indeed in anything that can be interrogated within the system, for example, a test tube of copper sulphate crystals suspended above a Bunsen burner fl ame and observed.
It is not sensible to ask which of these ways of understanding the scenario is cor- rect, as both are meaningful and potentially useful ways of thinking about the situ- ation. There is a difference of semantics here, as ‘knowledge’ is understood rather differently in these two ways of modelling the same situation – either as internal resources of the learner or as distributed across a network of people/things. The question is: Which is the most useful way of understanding this situation? That is likely to depend upon our purposes.
The distributed cognition perspective offers a useful way of thinking about knowledge. However, seeing knowledge as distributed across a network may lead to
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Fig. 9.4 The individual learner supports cognition by drawing upon external resources
rather convoluted notions of what the knowledge actually is in particular networks.
Consider a specifi c example of the scenario referred to above (Fig. 9.6 ).
Figure 9.6 represents a very simple example of a situation we might examine through the lens of distributed cognition. Two classmates, Jean and Jerome have got together to revise for a test are looking at a diagram provided by the teacher entitled ‘the structure of NaCl’. From a distributed cognition perspective, knowledge might be said to reside in the diagram. However, it is not clear that it is possible to assign any specifi c knowledge to the diagram in isolation (and from a distributed cognition perspective, one would not wish to, as knowledge is distributed across the system).
Now, in an ideal world, Jean and Jerome would look at the diagram, and discuss it, and come to an agreed interpretation of it. Indeed, in an ideal world, they would interpret the diagram in the way the teacher had intended! If Jean and Jerome appre- ciate the value of talking through their ideas, and if they have developed critical thinking skills in argumentation (see Chap. 7 ), it is quite possible that the diagram may be a useful resource to facilitate a discussion through which both learners develop their understanding of some science and so modify their own cognitive
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Fig. 9.6 The knowledge residing in an object
Fig. 9.5 Distributed cognition perspective: the learner is one component of a more extensive cognitive system
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structures. It is also in principle possible that due to the interactive and iterative nature of dialogue, they may shift their views towards much the same understanding of the science – leaving aside for the moment the issue of how we could ever be sure they had the ‘same’ understanding (cf. Chap. 6 ). In this situation we might see that the two ‘processing’ components (i.e. Jean and Jerome) coordinated effectively, supported by the external resource of the diagram – and so indirectly drawing upon the teacher’s own input – and consider this an effective example of distributed cognition. If the intention had been to write summary revision notes together, an agreed output might readily be produced.
However, without in any sense undermining the value of peer discussion and dialogue in learning, it is also clear that the ideal case is not the only, and perhaps not the most likely, version of this scenario. Both Jean and Jerome have unique internal (cognitive) resources for interpreting the diagram, and they may come to different interpretations. For example, perhaps the teacher was intending to represent a cross-sectional slice through a 6:6 coordinated crystal, showing the cubic arrange- ment of ions, but Jean fails to appreciate the sectioning and reads the diagram as showing that NaCl is an ionic crystal with 4:4 coordination. Jerome, however, holds some very common alternative conceptions of ionic bonding (used as an example in Chap. 6 ) and interprets the diagram as showing how ten molecules of NaCl, formed by electron transfer between Na and Cl atoms, are neatly packed into the crystal.
Jean and Jerome might discuss their different interpretations but will not necessarily come to an agreement or even fully appreciate each other’s ideas.
In this less than ideal version of the scenario, it is still possible to consider distributed cognition as it would be possible, for example, to consider a transcript of the conversation, and start to build a model of how the two students were thinking, and perhaps observe some shifts in position. However, this would not lead to a clear outcome, beyond perhaps simply an agreement to differ and move on to the next task. So the distributed processing could certainly be modelled, but as there is more than one self-directed ‘processing component’ involved, the model would be a messy one. In this situation, the case for preferring to see the scenario as a distributed cognitive system rather than two interacting cognitive systems may not be strong.
In more complex contexts such as groups of learners working together or whole classes, the notion of what the knowledge that is distributed across the system will become even more problematic.
However, these alternative conceptualisations could both offer valuable insights, and it is not argued here that the distributed cognition model does not have value, but simply that because people each have their own goals, and the ability to moderate them, and are able to direct their own behaviour, any distributed network of people becomes a complex situation as there is no one source of executive control marshalling the distributed resources towards a common purpose, and able to make executive decisions when different processing components are unable to agree. Teachers, who might like to be in that position in regard to their classes, are well aware that the individual ‘processors’ (students), having their own minds, are not always prepared to overwrite the outcomes of their own internal processing on the basis of external authority! Distributed cognition offers a useful perspective to explore and analyse
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