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Everyone has favourite education graphs, right?

A picture is worth a thousand words. I’ve been thinking about this while reading an excellent book on data visualisation and it’s got me thinking about my favourite education graphs.*

In no particular order, here are some of my favourites.

Phonics Screening Check

The phonics screening check is intended as a light touch assessment to promote systematic, synthetic phonics teaching and to identify pupils who have not met a basic standard.

This graph depicts the number of pupils who score at each mark from 0 – 40.

What’s striking about this graph is the ‘unexpected’ bump of pupils who just achieve the pass mark of 32. This graph illustrates why you cannot have an assessment that is high stakes for a school and also have teachers administer and mark the assessment.

Month of birth lottery

Everyone in education is aware of various inequities. One that is often overlooked – especially in secondary schools – is the considerably worse outcomes for younger children within a year group.

This simple analysis from FFT Datalab shows the percentage of children achieving the expected standard split by month of birth.

The percentage of children achieving the expected standard organised by month of birth using data from the 2016 KS4 cohort.

An interesting question is what should be done at a school and pupil level to account for these differences? For instance, primary school headteachers often remark that they have an unusual number of summer born pupils in some year groups – should we adjust league tables to account for this?

At a pupil level, Higgins and Elliot Major have advocated adjusting the test scores of summer born pupils.

Finding schools that misuse ECTs

Training teachers is expensive for the state, our education system and individual teachers.

Therefore, it is concerning to hear anecdotes that some schools seem to churn through Early Career Teachers, which often results in them leaving the profession.

The question is, how do we effectively identify these schools amongst the messy noise of school data?

In a great piece of detective work, Sims and Allen apply funnel plots – often used in meta-analysis – to identify schools warranting further investigation.

Crucially, their analysis accounts for the size of the schools. In total, they identified 122 schools in England that employ and then lose an unusually high number of ECTs. If these schools were like the national average, it would have resulted in 376 additional teachers remaining in the profession.

*If you do not have favourite graphs you’re either in denial or missing out on one of the great joys in life.