ABSTRACT
I began my research career in 2001, following 10 years as a classroom teacher in a comprehensive secondary school in England. During the 1990s schools were changing fast. Following the introduction of the National Curriculum in the late 1980s, national tests for 7-, 11-and 14-year-olds were introduced. This was part of the establishment of quasi-markets in education engineered through the inculcation of a new education culture characterised by managerialism and performativity (Ball 2008). One of the principal technologies of this new culture in schools was the performance or ‘league’ table of results. Such league tables were being made possible by the rapidly expanding capacity to gather, store, manipulate and present data electronically. So statistics, ‘the language of politics and persuasion’ (Dorling and Simpson 1999: 1), became an increasingly powerful tool for reforming education. My attention as a new teacher was now not only on the individual learner in my classes, but also on the contribution that each would make to the group results and therefore my performance management. This period marked a shift in the politics of schools, in which economic imperatives were increasingly at the heart of a new Standards agenda. A key ingredient in this shift was the increased use of data. Anyone familiar with education in England in 2009 will be all too aware of the tide of
data that flows into and out of schools. Engagement with this data is unavoidable. The work of teachers and pupils and the choices of parents in this education market are now framed by the use (and abuse) of data (Goldstein 2001). My research tries to embrace the tensions of researching both the texture of learners’ daily experiences and the depersonalised bigger pictures that get painted with statistical data. Here, I will be talking in particular about the Department for Children, Schools and Families’ (DCSF) National Pupil Database (NPD). This database includes the records of attainment, schools attended and social data for all students in England. The database will soon have grown sufficiently large to enable the tracing of an individual student’s educational trajectory from preschool through to 18 years of age and will, in the future, also be linked to higher education data. Needless to say, this is a vast dataset. You might be working with other types of large datasets (e.g. questionnaires) and many of the issues raised here will be the same.
