When Duncan Baldwin contacted me over a year ago with a random message “Claire, it would be useful to have a conversation with your developers/boss”, we at SISRA Limited were really intrigued. The result of that conversation? An exciting exercise which culminated in over 1100 schools agreeing for us to use their anonymised data to create Attainment 8 estimates long before the release of official figures.
The feedback from schools was outstanding. One Headteacher sent me a text when the very first set of collaboration A8 estimates became available. He was feeling “a little bit emotional” as his school’s P8 had risen by 0.29 from the previous year. It was hearing stories like this that confirmed to me that collaboration was the right direction for education data and for our schools.
Moving on a year, following more conversations with Duncan and hundreds of schools about further collaboration ideas, the 2018 collaboration is bigger and better.
We’ve been told time and time again that schools have been frustrated about not being able to predict accurately due to new specifications and even on results day, they wouldn’t actually know how they had done compared to others around the country. Wouldn’t it be great if schools were able to compare how a qualification taught at their school did against other schools? As we (SISRA) discussed this more, the juices flowed and the idea of subject transition matrices, as well as a subject progress index (SPI), evolved. (SPI – A SISRA-exclusive measure showing how each of your pupils have performed in each subject compared with all students with the same KS2 Prior in our Data Collaboration. Think Subject P8 but better!). This new feature could also be used for reflection on the previous year when schools are doing exam analysis and planning the year ahead.
Of course, to do something like this, data staff would need to do a small amount of administration. SISRA Analytics is extremely clever but not clever enough to know that a subject called Computer Science is actually AQA GCSE 9-1 in Computer Science (QN Code 60183019). But by completing some simple steps, and ensuring that all data is correct, it’s possible to take part in future collaborations and access estimates and features close to the exams period.
Collaboration relies on schools being willing to take part in such an exercise, but it’s also imperative that the data used is suitable and accurate. The more schools that opt-in and follow the collaboration steps the more accurate the estimates become and the more insight schools will have on progress. The scope for expanding the data collaboration is incredibly exciting but the basic principle of good accurate data applies.
In my next blog, I’m going to share some tips and preparation for collaboration. It’s worth noting that these pointers aren’t collaboration or SISRA specific – they can be used by any data administrator to make life a little easier!
If you have any questions regarding the Data Collaboration, or are looking for support with your Analytics set-up, please email us on email@example.com.