ABSTRACT
One of the major hurdles for schools using value-added data for school improvement purposes, at whatever level of contextualisation, is the time lag between the publication of results in the summer and the issuing of validated1 value-added data by the DfE the following January (typically) when it is uploaded onto RAISEonline (though the Fischer Family Trust uploads its unamended value-added data late in October). One way around this time-lag is to calculate a kind of ‘quasi VA score’ for each student by subtracting the estimate2 for the student at the end of the Key Stage from the actual outcome scores achieved in National Curriculum tests or GCSE examinations. For individual subjects, progress can be calculated by converting each grade to a points score and again subtracting the estimate for that subject from the actual attainment. For National Curriculum tests at KS2, this would be a little more complex as it would require calculating a fine-grade level from a knowledge of the raw scores that correspond with each of the level thresholds, which information is normally published and available to schools shortly after the close of the marking process. And the results of optional National Curriculum tests for students in Year 9 could be treated in the same way. ‘Actual-minus-estimate’ scores are quasi scores because they are not based on an analysis of the progress of students within a specific cohort, but rather the progress made by each student compared with his/her estimate, and these estimates are based on the progress made by students in the previous year’s national cohort. It is important to recognise that actual-minus-estimate scores may therefore vary from true VA scores published later in the year, but that notwithstanding they allow teachers in schools to produce measures of progress at the student level
fairly quickly after results are published and thereby seize the initiative of using data to inform improvement and development plans for the coming academic year. Group-level scores (for example by gender, FSM entitlement, ethnicity or social class) can be produced by simply calculating the mean score for all students in that particular group.
