Science, authenticity and modelling

Science, authenticity and modelling

Following from the radical reassessment of the nature of science by, among others, Hanson (1958), Kuhn (1996) Lakatos (1970, 1974) and Feyerabend (1978, 1987), authentic science has been characterised as involving, or at least allowing, the following elements: working and learning in con- texts constituted by ill-defined problems; the tolerance of ambiguity and uncertainty; and the expectation that theories may be challenged and ultimately discarded. Individual learning of science is characterised as a ‘sense-making’ activity predicated on current knowledge, with learners participating in communities of enquiry in which they have opportunities to draw on the expertise of more knowledgeable others (Roth 1999). Roth associates ‘authenticity’ of learning activities with a view of learning as a ‘situated’ activity and contrasts this to the artificial nature of most school ‘problems’. ‘Out of school problems’, he argues, ‘are not “set” . . . [and] have to be framed as problems before they can be solved. In many cases, there are no prospects to get a “right” solution’ (Roth 1999: 14).

then, they must be involved in the development and application of theory

For learners’ experience of learning and doing science to be authentic,

(taken here not necessarily to mean the formalised predictive theories of science, but concepts, models and counterfactuals) and the ‘ways of think- ing and practising’, the ‘particular understandings, forms of discourse, values or ways of acting’ (Hounsell and McCune 2004) of professional scientists. One of these particular and characteristic forms is modelling. Scientists and science teachers use a range of types of model (verbal, visual, gestural and concrete, amongst others) as they conceptualise, problem- atise and discuss complex concepts, processes and relationships. In this respect, modelling represents a characteristic ‘form of discourse’ but they also represent a pattern of engagement with ‘real world’ domains and problems rather than with a curriculum of predefined problems with ‘right

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answers’. There is no ‘right’ model for helping to understand a given situation or problem – just a ‘currently-best-in-my-opinion’ one.

Models have a role to play at every stage in the scientific process from prototyping and ‘what-if’ statements through to ‘textbook’ reifications of concepts or processes. Boulter and Gilbert (1998) differentiate between notions of ‘mental’ and ‘expressed’ models – for them a model is a repre- sentation of an object, event, process or system; mental models are per- sonal, private representations of the target; expressed models are placed in the public domain. Aspects of science, and of the science curriculum, are characterised by different kinds of model and different modes of expres- sion (see Boulter and Buckley 2000 for a useful typology) and any attempt to foster authentic learning in school science needs, therefore, to involve the incorporation of appropriate models. Modelling’s claim to a place in the curriculum, however, is not based solely on its being an authentic activity; there is a body of evidence (DiSessa 1986; Mellar 1994; White and Fredricksen 1998) which suggests that a modelling-based curriculum also has the potential to leverage important changes in classroom culture and levels of learner engagement and autonomy. Interestingly, it has been argued that, in most current school contexts at least, Design Technology, with the patterns of modelling it involves and the opportunities for the learner to be designer, maker and evaluator, presents greater opportunities for an authentic role for modelling than does school science (Gilbert et al. 2000).

Models also have a role to play in learning beyond merely acting as illustrations or as simplifications of complex situations. Johnson-Laird (1983) describes how inferential reasoning (another key ‘way of thinking’ for scientists) involves an iterative process in which mental models are progressively elaborated and new ideas generated. This view is advanced by Gentner and Gentner (1983) who, in their work with high school and college students, demonstrated how analogical models are conceptual tools capable of generating new understanding through a process of mapping of features from one domain to another. Nersessian (1992) goes further still by arguing that, in the work of professional scientists, it is analogical reasoning that ‘do[es] the work’ of problem solving, rather than simply acting as a guide or a heuristic device.