measuring the outputs and outcomes of vocational training

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14 tors according to whether they speak to learning outcomes, circumstances that shape these outcomes or constraints that set policy choices into context. Policy issues (such as equity in educational outcomes and educational opportunities, the quality of educational outcomes and educational provision, effectiveness of resource management) are dealt with in the same framework. With the strong support of new statistical tools, the OECD moves from the well-known, classical approach of a framework of Input-Process-Output-Outcome towards a more policy-oriented, empirical approach, where new concepts such as ‘policy levers’ or ‘policy space’ are introduced to outline the multi-faceted relationships between data generated with the new tools and the policy analysis of various educational themes. One can distinguish in this approach a combination of three different dimensions: statistical, analytical and policy-oriented. As the main data provider on education and training for EU member states, Eurostat has recently embraced a linear approach to describe the education process, as shown in the figure (from a recent document on “the way forward” for education statistics). Inputs Expenditure Funding Entrants Students Teachers Process Schools Classes Educ. Methods ICT usage Teacher Training Working time Violence … Outputs Graduates Early leavers Dropouts Outcomes Skills Indiv. Returns Eco. Returns Social returns A combination of the two approaches will be used to organise the indicators on outputs and outcomes of vocational training. It is hoped that this will better target data needs, thereby making reporting more explicit and customised for different users. The framework does not aim to reflect a consensus among professionals on how to map the measures of the current state of vocational training internationally, but rather will try to advance the debate on indicators and their framework.
In defining the components of such a framework, a first step will be to define the education and training variables that will form the analytical component of the framework. The variables will need to respond to issues that are high on policy agendas (i.e. Council Conclusions or Commission Communications) and where the comparative perspective can offer important added value to what can be achieved through national evaluation exercises. This analysis should also shed light on national circumstances or constraints that contextualise the policy choices. The association of the background variables with the survey variables have been successfully used in PISA with the help of two composites. 5 Beginning the process of definition at the analytical level will also help to identify topics which are present on the political agenda or in key political messages but which have not yet been (sufficiently) examined or empirically supported; the analytical component could be strengthened with new data or through new combinations of existing statistics (i.e. with the help of indexes). Additional statistical techniques (such as sensitivity analysis) could be of help in identifying the variables that have been successfully used in the analytical work and should therefore be refined and included in the new data collection exercises. Finally, a well-defined analytical dimension would allow for a better choice of proxy measures for outputs and outcomes of vocational training. In the example of educational attainment level as a proxy for skills, doubts about the most appropriate interpretation of the figures are, to some extent, borne out by the PISA results. Four new Member States participated in IALS between 1994/94 and 1998/99 (the Czech Republic, Hungary, Poland and Slovenia) and PISA was carried out in four countries (the Czech Republic, Hungary, Poland and Latvia) in 2000. Of these countries, only one participant in both surveys (the Czech Republic) had results close to (and, in some skills areas, above) the average of participating countries. In Hungary, Poland and Slovenia very high proportions of the populations aged 16-65 had low functional literacy skills (at Level 2 or below on a five- point scale) as measured in IALS – especially for prose and document literacy. Similarly, in both Hungary and Poland nearly one in two (and in Latvia three in five) of all 15-year-olds had low reading literacy skills (at Level 2 or below on a five-point scale) as measured in PISA. Science literacy skills (and to a lesser extent maths literacy) among 15-year-olds and quantitative literacy skills among adults aged 16-65 were slightly better (Kennedy and Badescu, 2004). 5 The PISA index of economic, social and cultural status was derived from a set of family background variables such as: the highest international socio-economic index of occupational status of the father or mother, the highest level of education of the father or mother converted into years of schooling, the access to home educational and cultural resources (e.g. books, computers, internet connection, other learning tools). The PISA index of instrumental motivation was derived from the frequency with which students study for the following reasons: to increase my job opportunities; to ensure that my future will financially secure; to get a good job. In a framework on outcomes of vocational training a variable such as the social background of students should also include, besides the socio-economic index (which could be used as a composite measure of parental status). measures of antecedents such as: participation of parents in vocational training, unemployed persons in the same family or occupational career of parents.
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