Love it or hate it, schools are all about data. We read, create and discuss data. We review the data and then do it all again. All this data packs a punch, but it is not ambitious enough for our students. Instead, current data illustrates exactly what is not working for them.
Exclusions on the rise, inequality in our classrooms, school avoidance, a soaring crisis in mental ill-health; the data we base our daily interactions on is not supporting our students, and we need to turn the tables (pun intended).
A pixelated picture
Data is central to the functioning of schools. From analysing test scores to monitoring attendance, data permeates every aspect of education. However, the way we use data can sometimes be detrimental to our students’ well-being and growth.
We often prioritise our time discussing academic performance data over students’ holistic wellbeing. Despite citing wellbeing as a top priority, we spend very little time and energy on data related to belonging and barriers to learning.
This narrow focus on subject grades and attitudes to learning fails to capture the richness of their experiences and passions – why they do not or can not learn. Additionally, it perpetuates inequalities and marginalisation, especially among our most vulnerable students.
If we don’t separate their lived experiences from their performance, we rely on individual teachers to connect with them about their passions. This is far from optimal, especially if we don’t capture those relationships. Worse, it makes it less likely, as institutions, that we can systematically remove the barriers they face.
In short, our focus is resolutely trained on what young people are getting wrong. To address this, we need to rethink our approach to data literacy and data collection. Schools must become more adept at understanding and interpreting data, moving beyond traditional metrics to capture the complexity of students’ lives.
Street view
In the main, schools collect two types of data: ‘satellite’ or large-grain data (patterns for cohorts or for individual students across the school), and ‘map’ or medium-grain data (student performance, progress, attendance, behaviour, etc.).
But there is a third level of data science. One example, being used by US educators focused on equity and anti-racism, is ‘street’ data. My research on supporting under-served students echoes the need for more of this finer-grained information to really see our students.
We know big ‘satellite’ data and performance trends are deeply flawed when it comes to wellbeing, inclusiveness and equity. It lacks nuance and context, which leads (ironically) to a loss of learning for those who the data is meant to serve.
Likewise, ‘map’ data does not give us enough feedback to make quick and deep decisions. It lacks the humanity we are desperate for in our data warehouses.
By contrast, equitable ‘street’ data captures their views, their narratives and gives us an analytical advantage in creating positive and inclusive cultures.
Eyes on the prize
Our current use of data is leading us down dehumanising and potentially dangerous dead-ends – not just an increasing reliance on AI, but surveillance too. We need to rebuild our relationship with it, not by deleting what we have but by rethinking its intentions.
We could stand to learn from new approaches like ‘warm data’, for example, which captures the complex web of meaningful and impactful relationships in our organisations.
We could better train school leaders and teachers about survey design and data visualisation. This would raise inclusivity and accessibility, but also ensure we more effectively communicate our insights and impact.
And if we’re going to do these things (and we should), then it needs to start from the top. Labour’s new accountability scorecard – on which parents of marginalised students have been consulted – will be an early opportunity to bring these changes about.
For too long, we have focused our work on meeting the needs of our dashboards. It’s time for our dashboards to meet our needs and our students’. It’s time to humanise our data.
To that end, rebalancing accountability must not amount simply to more data. Foremost, it must be about new ways of understanding, collecting and understanding it.
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