Data is the enemy. That is the sometimes discreet, but often overt, message wrapped up in the workload crisis affecting teachers; children have been turned into digits and their education is now filtered through a computer screen in a way that resembles something from The Matrix.
Numbers waste everyone’s time and dehumanise learning.
I don’t subscribe to this view. I believe data has been scapegoated to divert attention from the real issue: bad leadership of data. Because used well, data is not our enemy, it is our ally.
The purpose of collecting data should be simple: to identify those who might be underperforming, to pinpoint any possible issues and to put in place a sensible intervention. There should be a predictive element of data collection, too. While predicting student outcomes is difficult, direction is still needed for all. So however coarse our attempts to predict may be, they are still needed, as they will identify the students who are most at risk.
Good leadership of data - and thus the key to making data useful in schools - relies on two things: teacher honesty and the meaningfulness of the analysed data at all levels, but in particular to the teacher.
Teacher honesty
There are so many perverse incentives that encourage teachers to report dishonestly: performance management, reputation, observation grading, getting a student group into an intervention session, parental pressure, avoidance of looking like a failure, overconfidence…and many more.
Those teachers that have succumbed to these pressures will suspect others of doing so, too. Even those who have just been tempted will suspect others followed through with it. This not only distorts data, but also decreases trust in the data. It becomes a spiral: teachers trust it less, they suspect more and more people are being dishonest, and become more prone to dishonesty themselves.
How do you fight this? An environment of trust is key. This requires teachers to report data honestly, no matter what story it tells, and for that to be met with discussion and mutual examination of the circumstances; a desire to help, not persecute; and a focus on the children.
Meaningful analysis of data
If the information is not used, then teachers will likely lose interest in providing the data. But if teachers can see that other professionals in the school are using their data to help children, it becomes motivational. And if the class analysis can be produced so that it “shines a light” on the teacher, then even better.
So the analysis of data should produce appropriate layers of information for the differing needs of the audience with the child at the core and the teacher a prime user. And it is only when we piece together all the data about a child that it can point us in the right direction to support them. As a minimum, data on attendance, behaviour and academic performance should be brought together in one place so that we can see the full story.
Often, when teachers say data collection is mindless or pointless, or is contributing to their workload, it is not because the process of data collection is at fault. Rather, it is the way that it is done, how it is used and the environment in which it is collected.
Data collection is necessary because it drives teaching and learning for the benefit of the child. If we throw it out in pursuit of the easing of workload, we are really missing the point of what we are here to do as teachers.
Elly Blake is vice-principal for academic standards at Q3 Academies Trust
This is an edited version of an article in the 21 September issue of Tes. Elly gives a detailed guide to how she ensures this advice is followed in her schools and some tips on predictive grades in the full version, which you can access here.
