Why teachers should stop worrying and learn to love the d-word

In many schools, “data” is a dirty word. But it shouldn’t be. Learning First manager Tim Lambert explains how we can make data great again.

September 01, 2017 | Tim Lambert

Many teachers don’t like “data”. At best it sounds technocratic and soulless. But there’s another way to look at the word.

Long before NAPLAN and MySchool, before performance frameworks and international survey rankings, teachers assessed and graded, and administrators recorded attendance and attainment. This was information, evidence – data. But teachers did not fear that this information would be used to judge their performance.

Today governments want to see constant evidence of return on investment.  Key Performance Indicators, surveys and assessments try to capture all aspects of school performance. The word “data” entered the school lexicon, born of the computer’s ability to collect oceans of information. At some point in the staffroom conversation “data” and even “evidence” took on sinister connotations, related to accountability and compliance.

And that is a pity, because call it what you will, using evidence or data to relentlessly examine what’s working for our students is at the heart of great teaching. Teachers and school leaders need data to make informed decisions about where students are in their learning, and what they are ready to learn next.

It’s true that too much data can be a barrier to good decisions. Data can also give us information that isn’t useful, or is difficult to interpret and apply. So how do schools use data or evidence to actually support teaching and learning decisions made every day, in every classroom? Here’s an educator’s rough guide.

1.  Measure what matters. There’s a temptation to collect data about whatever is measurable, because the systems are there to collect it, and it’s bound to be useful. But while technology can cope with a blanket approach, that blanket can be stifling for staff. Good leadership involves setting clear priorities, and actively managing distractions that eat up time and energy.

What matters? Students’ learning needs, first and last. If you are crawling over data that doesn’t tell you where your students are and what progress they are (or are not) making, you are wasting time and effort. Be specific. There are plenty of things that define your priorities: the school annual plan, the annual report, the school review report, subject or year level plans. In the classroom, decide which priorities are the most important for your students, picking only one or two to focus on at a time. Then work out what data and evidence will show how your students are going against these.

2.  Collect the data that matter. For teachers, these are data that help to improve teaching and learning. A load of other data ‘matters’ because it relates to compliance and management issues, but leave that to the administrative leaders. Teachers and instructional leaders should be concerned with data about students’ learning. As academic educator and writer Patrick Griffin says so well:

 Evidence is what (students) do, say, make or write. There are no other forms of observable evidence that we can use in the classroom.

In other words, the data that matter form the bread and butter of teachers’ work. Collecting and analysing data shouldn’t feel like a burdensome “extra” on top of classroom practice. It should be a deeply integrated into the routines of planning, teaching, assessing, and seeking and providing feedback.

3.  Use data for good, not evil. If data do strike fear into the hearts of staff, it’s because they are not being used properly. The evidence is objective. It doesn’t judge or condemn, it just tells you how things are. That’s how data should be used. If you are working with colleagues who are committed to doing the best for their students’ learning, it makes sense to use data and evidence to have constructive, challenging conversations about what does and doesn’t work in the classroom, and why.

4. Use data to ask questions first, and draw conclusions last. If your NAPLAN, STAR or PAT-R data indicate problems with students’ learning in a particular area, embrace the heads-up, and start investigating. Find out directly from your students about what they understand. Sit down with colleagues and samples of students’ work, and talk about what you see. Compare work by the strugglers, the stars, and the kids in the middle (who probably don’t get talked about enough). Discuss what you would expect to see from a student who has comprehensively met the achievement standard. Talk about what and how you currently teach this content, and what you could learn together to strengthen how you teach it. And if the data tell a consistent story about great student progress, use the evidence to celebrate the success.

“The big idea is that evidence about learning is used to adjust instruction to better meet student needs – in other words, teaching is adaptive to the learner’s needs” Dylan Wiliam 

Teachers and school leaders should reclaim “data and evidence” as a key part of their professional practices and judgements that they make, based on daily interaction with students they care about, and thoughtful assessment of the work. Be deliberate and focussed in your data collection. Allow it to challenge and improve your teaching. As British educator Dylan Wiliam has said, “When teachers do their job better, their students live longer, are healthier, and contribute more to society.”

For practical guidance on using data and evidence to improve teaching and learning, see:

Griffin, ed. (2014) Assessment for Teaching, Cambridge University Press

Parker Boudett, City, and Murnane, eds. (2013)  Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning, Harvard Education Press

Wiliam (2011) Embedded formative assessment, Solution Tree Press

Tim Lambert is a Manager at Learning First.

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Many teachers don’t like “data”. At best it sounds technocratic and soulless. But there's another way to look at the word. Long before NAPLAN and MySchool, before performance frameworks and…

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