//Higher attaining disadvantaged pupils need help to keep up

Higher attaining disadvantaged pupils need help to keep up

By |2017-03-03T09:48:04+00:0023rd October 2015|Pupil demographics|

The TES reported that Pupil Premium funding for higher attaining disadvantaged pupils may be redistributed in order to give extra support to lower attaining pupils. While such a move might be in the spirit of the Pupil Premium to reduce attainment gaps, data suggests that higher attaining disadvantaged pupils still need additional support to keep up.

In Chart 1 we compare the attainment of pupils from low, middle and high socio-economic status (SES) backgrounds at ages 7, 11 and 16 using FSM eligibility and 2001 OAC

[i] code of home postcode at age 7. Attainment has been standardised onto the same scale.

We then looks at 3 groups with similar KS1 starting points:

  • Level 3 in each of reading, writing, maths
  • Level 2B in reading and writing, level 2A in maths
  • Level 1 in reading and writing, level 2C in maths

Chart 1: Attainment at 7, 11 and 16 by socio-economic status

Now, it’s important to recognise here that we are not suggesting progress is linear- we’ve dealt with this before. Although the group averages are linear, individual pupils make progress in a more idiosyncratic manner.

Chart 1 shows that pupils who start off with high attainment at KS1 tend to dip and those with low attainment tend to improve. This is an example of regression to the mean in action.
But what it also shows is that pupils from low SES backgrounds tend to fall behind their peers from middle and high SES backgrounds with similar attainment at KS1. This is particularly the case for those with high attainment at KS1 (and there are relatively few of them to begin with).

So if Pupil Premium funding is to be weighted more heavily in favour of lower attaining pupils, an element perhaps ought to be retained to help higher attaining pupils keep up.

[i] http://www.ons.gov.uk/ons/guide-method/geography/products/area-classifications/ns-area-classifications/index/methodology-and-variables/output-areas/output-areas.html




About the Author:

Dave Thomson is chief statistician at FFT with over fifteen years’ experience working with educational attainment data to raise attainment in local government, higher education and the commercial sector. His current research interests include linking education and workplace datasets to improve estimates of adult attainment and study the impact of education on employment and benefits outcomes.

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