At the other hypothetical extreme, 100% of the variation is within-schools. In this case, every school would have the same average level of performance, no matter what students attend. All the variation in the population would be attributed to individual differences within each school. The interplay between the between- and within-school variance is complex and is influenced by such factors as: the cultural and socioeconomic diversity of the population; the clustering of students from similar cultural and socioeconomic backgrounds into schools; and the specialisation of curriculum provision in different schools (e.g. academic and vocational tracking).2 As already mentioned when examining gender differences, there is a considerable amount of clustering by gender in schools in Mauritius, the United Arab Emirates and, especially, in Malta with most of the 15-year-old population attending single sex schools. Another difference between schools that may relate to variance in performance is the structural changes within school systems that occur commonly around the age students are in the 15-year-old cohort: typically a change from lower secondary to upper secondary schooling. In Moldova, for example 50% of 15-year-olds were attending lower secondary, 34% upper-secondary and 16% mixed level schools. However, regardless of the complexities, one way to examine equity of educational provision and outcomes within a society is to examine the extent to which variation in performance is associated with the school which the student attends. Figure 4.7 displays the relative proportion of variation in performance within the population of each country that is accounted for between-schools. Results for the PISA 2009+ participants and their chosen comparison countries are displayed. The figure is sorted so that countries with the greatest proportion of between-school variance are at the top. As can be seen in Figure 4.7, OECD countries like Finland and Estonia, and the PISA 2009+ participants Georgia and Malaysia, have relatively little variance in performance associated with the school. In other words, students with different abilities are relatively evenly spread across the schools within the population. By contrast, in countries like Chile, Mauritius and Germany, students are more clustered into schools by ability. This clustering can arise from a complex of reasons: from deliberate decisions to track students into more or less academic streams based on attributes such as perceived ability; to an uneven socioeconomic distribution of the population into wealthier areas and disadvantaged areas serviced by local schools. However, regardless of the reason for relatively large between-school variation, the phenomenon of students being clustered into schools with other students of similar ability exists, and this is more prevalent in some countries than others.
2 Caution should be exercised when making international comparisons of between-school variance. The definition of what constitutes a ‘school’ is not always straightforward. For example, some countries will include all campuses in a multicampus school; other countries will not. Similar definitional differences may occur around multi-shift or multi ISCED level schools. While these differences in definition are not very relevant among PISA 2009+ participants, they do have implications for some PISA 2009 participants.
PISA Plus 2009
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