This report looks at top-performing students in the PISA 2006 science assessment, their attitudes and motivations, and the schools in which they are enrolled. Top-performers are defined as those 15-year-old students who are proficient at Levels 5 and 6 on the PISA 2006 science scale as compared with strong performers (proficient at Level 4), moderate performers (proficient at Levels 2 and 3), and those who are at risk of being left behind (proficient at Level 1 or below). 

The rapidly growing demand for highly skilled workers has led to global competition for talent (OEC D, 2008). While basic competencies are generally considered important for the absorption of new technologies, high-level competencies are critical for the creation of new knowledge, technologies and innovation. For countries near the technology frontier, this implies that the share of highly educated workers in the labour force is an important determinant of economic growth and social development. There is also mounting evidence that individuals with high level skills generate relatively large amounts of knowledge creation and ways of using it, compared to other individuals, which in turn suggests that investing in excellence may benefit all (Minne et al., 2007).1 This happens, for example, because highly skilled individuals create innovations in various areas (for example, organisation, marketing, design) that benefit all or that boost technological progress at the frontier. Research has also shown that the effect of the skill level one standard deviation above the mean in the International Adult Literacy Study on economic growth is about six times larger than the effect of the skill level one standard deviation below the mean (Hanushek and Woessmann, 2007) .

This chapter aims to shed light on the type of students who are top performers in science in PISA. Are they, for example, good all-round students, or do they excel just in science? Are males and females equally represented among the top performers? How well represented are students with an immigrant background or students speaking a language at home different to the language they use at school? Are students from less advantaged socio-economic backgrounds excelling? 

Having looked at individual and school characteristics of top performers in science, this chapter turns to the analysis of student experiences, attitudes and motivations. It investigates differences among performance groups and identifies what characterises top performers in science. The chapter is divided into four sections: The first describes student experiences with science teaching and learning as they relate to top performance; the second analyses the motivations of top performing students; the third reviews the aspirations of top performers in science for a future career in science; and the fourth and final section analyses a particular group of top performers in science, those relatively unmotivated. 

The statistics in this report represent estimates of national performance based on samples of students rather than values that could be calculated if every student in every country had answered every question. Consequently, it is important to have measures of the degree of uncertainty of the estimates. In PISA, each estimate has an associated degree of uncertainty, which is expressed through a standard error. The use of confidence intervals provides a way to make inferences about the population means and proportions in a manner that reflects the uncertainty associated with the sample estimates. From an observed sample statistic it can, under the assumption of a normal distribution, be inferred that the corresponding population result would lie within the confidence interval in 95 out of 100 replications of the measurement on different samples drawn from the same population.