If I told you a Year 9 student received fifty negative referrals last half term, what would you think?
It is a trick question.
I ask this in training sessions and I have seen a pattern in the answers. Many people say the student's behaviour needs looking into. But what if I then told you that all fifty of those negative referrals came from just two of the student's fourteen teachers?
What might we assume now? Perhaps those two teachers are struggling — perhaps they are new or newly qualified? But what if they were experienced, high-performing practitioners? Should we go back to assuming the pupil is the source of the problem?
What if a closer look revealed that the spike in referrals happened in precisely the two subjects the pupil had chosen not to take at GCSE?
Imagine if we were half as good at collecting, analysing and acting upon behaviour data as we are for academic progress data.
One reason we do not collect and analyse behaviour data as robustly as we should is that we do not fully trust it. We worry it is not accurate. But I would suggest that accuracy is not the point. Good data allows us to formulate the questions that help us identify the most sensible next intervention — for a pupil, for a teacher, or for a whole school approach.
Data points us towards the right questions. The answers require professional judgement. But without the data, we are working in the dark and calling it instinct.
Consider what behaviour data can tell you:
Which pupils are generating the most referrals — and has that changed recently?
Which staff are generating the most referrals — and are there patterns in subject, year group, or time of day?
Which locations in school generate incidents — corridors, the playground, certain classrooms?
Which times — first thing, last thing, after lunch?
None of these questions can be answered from memory or gut feeling with any reliability. They need data. And the data, however imperfect, is your friend — whether it tells the complete truth or not.