KS2 performance tables 2017: Three things from this morning’s data

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Today’s Key Stage 2 Statistical First Release provides further information on the 2017 Key Stage 2 results which we first wrote about here. This includes school-level data, plus data on the performance of different groups of pupils.

There are some interesting differences in progress scores between ethnic groups

There is much greater variation in the mean progress scores of different ethnic groups in maths than in reading and writing. For example, there was a gap of 3 scaled scores points between the lowest performing group (Gypsy/Roma) and the highest performing group (Irish) in reading. In maths, however, the gap between mixed white and black Caribbean pupils and Chinese pupils was almost 6 scaled score points.

Black Caribbean and mixed white and black Caribbean pupils tended to achieve below average progress scores in mathematics. Although Pakistani pupils tended to achieve above average progress scores in writing and maths, they tended to achieve below average scores in reading.

A gap is emerging in high attainment between disadvantaged pupils and their peers

In 2017, there was a gap of 7 points between disadvantaged pupils and their peers in the percentage of pupils achieving a high level in all three subjects (a scaled score of 110 or more in reading and maths, and working at greater depth than the expected standard in writing). This was an increase of 2 percentage points compared to 2016.


The gap widened most in mathematics, from 10 percentage points in 2016 to 14 percentage points in 2017.

Summer-born pupils catch up (a bit)

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There was a gap of 3 scaled score points between September- and August-born pupils in reading, and a 2 point gap in mathematics. This would have been at least 1 point wider had August-born pupils not caught up slightly during Key Stage 2.

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By | 2017-12-15T15:17:14+00:00 December 14th, 2017|Exams and assessment, School accountability|

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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