Multidimensional Data Analysis towards Assessing the European Education Systems

Abstract

By considering the fact that education in an important component within the context of Europe 2020 strategy, this paper aims at developing an aggregated indicator towards the assessment of European education systems, thereby being employed multidimensional data analysis techniques, namely principal component analysis.Withal, by using unsupervised classification techniques, specifically cluster analysis, the European countries will be grouped acording to the valuation of education systems. There was selected a sample consisting of 26 European countries, the data corresponding for the year 2012, being considered the following variables:school expectancy; the percentage of all 18-year-olds who are still in any kind of school; the total number of persons who are enrolled in the regular education system; the share of the population aged 4 to the age when the compulsory education starts who is participating in early education; mobility of students in Europe; pupil/teacher ratio in primary education; the average number of foreign languages learned per pupil in secondary education; the share of 15-year-old pupils who are at level 1 or below of the PISA combined reading literacy scale; early leavers from education and training; lifelong learning. The utility of current research is emphasized by the valuation instrument provided to the government authorities which could rank the European education systems.

  Keywords: International standard classification of education (ISCED), Programme for international student  assessment (PISA), principal component analysis (PCA), cluster analysis.

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