Creating Value In Schools
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In the podcast episode Who Gets to Belong in Schools?, Hannah Wilson speaks about data as a starting point for raising questions and evaluating belonging in the school setting.
‘You can’t measure it in isolation’, says Hannah. Belonging is a golden thread that ‘weaves through everything’.
‘It is your attendance data, your behaviour data, your attainment data, your progress data, your progression data, your recruitment data… the list goes on.’
Watch this four minute clip where Hannah talks about:
Prefer to read the transcript? Scroll to the end of this article.
Hannah Wilson: [00:00:00] We talk about it being a golden thread, that weaves through everything and ultimately to measure belonging, you can't measure it in isolation.
It is your attendance data, your behaviour data, your attainment data, your progress data, your progression data, your recruitment data, like the list goes on. And actually like, you need to be cutting every set of data through an intersectional lens. And then looking at what, I'm an English teacher, like what story does this data tell you?
And like, I'm working with a lot of leaders where the data is there, it's very loud, it's telling you very strongly, we've got a problem or we've got something we need to pay attention to. But often it's easier just to ignore it. So we talk about staff attrition. For example, some leaders innocently say to me, oh a lot of our black teachers are leaving this year.
And then I'll move on to the next thing. Hang on a second. Can we just go back to this for a minute? So you've got 10 black teachers leaving this year. Have you asked them why they're all leaving? Well, no, they, they're all just leaving. They've got new jobs. But why are they leaving? [00:01:00] My red flag is have you got a problem with racism in your school?
No, I don't think so. You don't think so? Well, how are you gonna find out whether you have or you haven't? And for me that's what I mean about the line of enquiry, the spiral of enquiry. The data's often there. We're just not looking at it. And I think sometimes, like I talk about data, like we're data rich when it comes to the pupils.
We're often data poor when it comes to the staff. The data often sits in lots of different platforms and pots. It's how we bring it all together and harvest that data, but also process that data. And like, I don't think we need any more data about the lack of racial representation in the UK workforce.
Like if I see one more report about it, like we know the situation, what are we doing with that data? What are we going to do differently to actually change the outcome? Because we keep doing the same. We'll keep getting the same.
Liz Worthen: Yeah. Brilliant. And can you, just going back to your point about data, and you talked about intersectionality in data. So can you just explain a bit for someone who maybe isn't so familiar with that term, intersectional. [00:02:00] What would that mean? Looking at your data with an intersectional lens?
Hannah Wilson: Okay, good question.
So, if we think about the term intersectionality, firstly, like we'd be very clear on paying homage to the person who coined that phrase. So Professor Kimberly Crenshaw, who's a Black American academic named that phrase and that concept looking at the specific experiences of black women in America, it was the General Motors redundancy rounds where a lot of people had lost their jobs.
And the people doubly disadvantaged were black women. So I'm just saying that because a lot of people are now talking about intersectionality, about any other overlapping traits that can really upset a lot of people. So I talk about intersectionality, I talk about intersectional identities.
So we all have an intersectional identity. And if I think about when I was a head department or head of year, I spent hours handling data. I had lots of spreadsheets. You often have that kind of the demographic header at the start of the spreadsheet with all the information about your pupils. For me, intersectional data handling is how you [00:03:00] filter, sub filter.
And you look at those trends. So say I was looking at 80 Year 11s and how they were performing in English, I could tell you how the boys and the girls were doing, but I could tell you how the black boys were doing and the white boys were doing, and the SEND boys. And that, that to me is the granulation of the detail.
So the intersectional data handling is looking at groups within groups. So if I give you a staff example, when I left headship, I worked in the university for a year running a PGCE. We had 450 trainee teachers in secondary, and I was given a list of 30, 32, I think it was, who had deferred the year before.
But no one had looked at like, what common traits do these people have? Every single one of them had at least one protected characteristic. So for me, that's the kind of the data handling that we're looking at the fact that, okay, 450 passed, what do they have in common? They all hold majority identities.
32 deferred. What do they hold in common? They all have a protected characteristic. If we then drill that down further, okay, five of [00:04:00] them were full-time parents and carers. So what are we doing to support them? Four of them have got a mental health condition. What are we doing to support them? So that to me is the kind of the intersectional data that we're, we're looking at the patterns and the trends.
We're identifying the barriers and we need to go into the sublayers of the data to do that.
