Education and Ideologies of the Knowledge Economy: Europe and the Politics of Emulation

The Austrian philosopher Ludwig Wittgenstein famously proposed a style of philosophy that was directed against certain pictures [bild] that tacitly direct our language and forms of life. His aim was to show the fly the way out of the fly bottle and to fight against the bewitchment of our intelligence by means of language: “A picture held us captive. And we could not get outside it, for it lay in our language and language seemed to repeat it to us inexorably” (Wittgenstein 1953, 115). In this context Wittgenstein is talking of philosophical pictures, deep metaphors that have structured our language but he does also use the term picture in other contexts (see Owen 2003, 83). I want to appeal to Wittgenstein in my use of the term ideology to refer to the way in which powerful underlying metaphors in neoclassical economics have a strong rhetorical and constitutive force at the level of public policy. Indeed, I am specifically speaking of the notion of ‘the performative’ in Wittgenstein and Austin. The notion of the knowledge economy has a prehistory in Hayek (1937; 1945) who founded the economics of knowledge in the 1930s, in Machlup (1962; 1970), who mapped the emerging employment shift to the US service economy in the early 1960s, and to sociologists Bell (1973) and Touraine (1974) who began to tease out the consequences of these changes for social structure in the post-industrial society in the early 1970s. The term has been taken up since by economists, sociologists, futurists and policy experts recently to explain the transition to the so-called ‘new economy’. It is not just a matter of noting these discursive strands in the genealogy of the ‘knowledge economy’ and related or cognate terms. We can also make a number of observations on the basis of this brief analysis. First, there has been a succession of terms like ‘postindustrial economy’, ‘information economy’, ‘knowledge economy’, ‘learning economy’, each with a set of related concepts emphasising its social, political, management or educational aspects. Often these literatures are not cross-threading and tend to focus on only one aspect of phenomena leading to classic dichotomies such as that between economy and society, knowledge and information. Second, these terms and their family concepts are discursive, historical and ideological products in the sense that they create their own meanings and often lead to constitutive effects at the level of policy. Third, while there is some empirical evidence to support claims concerning these terms, at the level of public policy these claims are empirically underdetermined and contain an integrating, visionary or futures component, which necessarily remains untested and is, perhaps, in principle untestable.

Transfer of spatial knowledge from a virtual environment to reality: Impact of route complexity and subject’s strategy on the exploration mode

The use of virtual reality as tool in the area of spatial cognition raises the question of the quality of learning transfer from a virtual to a real environment. It is first necessary to determine with healthy subjects, the cognitive aids that improve the quality of transfer and the conditions required, especially since virtual reality can be used as effective tool in cognitive rehabilitation. The purpose of this study was to investigate the influence of the exploration mode of virtual environment (Passive vs. Active) according to Route complexity (Simple vs. Complex) on the quality of spatial knowledge transfer in three spatial tasks. Ninety subjects (45 men and 45 women) participated. Spatial learning was evaluated by Wayfinding, sketch-mapping and picture classification tasks in the context of the Bordeaux district. In the Wayfinding task, results indicated that active learning in a Virtual Environment (VE) increased the performances compared to the passive learning condition, irrespective of the route complexity factor. In the Sketch-mapping task, active learning in a VE helped the subjects to transfer their spatial knowledge from the VE to reality, but only when the route was complex. In the Picture classification task, active learning in a VE when the route was complex did not help the subjects to transfer their spatial knowledge. These results are explained in terms of knowledge levels and frame/strategy of reference [SW75, PL81, TH82].