Skip to main content

Learning Data Conversations - A Catalyst for Building Collaborative Professional Cultures

We are awash in learning data in our schools. However, there remains a deep skepticism for quantitative data and, at the very least, a noticeable discomfort for sharing and analyzing quantitative and qualitative data about one’s own student learning. Teachers and school leaders who wish to begin looking at both qualitative and quantitative data in a systematic way for robust school improvement must begin to develop a professional culture that values talking about data of all kinds.

Investing in data literacy and data conversations serves schools in fundamental ways:

  • Embedding a collaborative learning cycle connected to student learning data and qualitative data as part of curriculum development and school improvement. 
  • Developing a shared language and safe environment for collaborative and open-minded exploration of ideas that might challenge our existing mental models. 
  • Challenging and influencing unproductive mindsets about student performance.
All of the above serve to develop and sustaining a high-performing collaborative professional culture.

In these collaborative cultures, data does not rule as a disconnected and unsympathetic entity. Instead, compassionate humans control how and to what end the data is used. Our learners, including our teachers, craft richer, deeper, more accurate stories that empower us all to take action in areas of need to improve learning for all.

Comments

Popular posts from this blog

From Mission to Metrics

Last week a new LAC school presented me with an interesting question: “ How do we know we’re meeting our mission? ”. It’s the kind of question that we often ask ourselves at accreditation season, but how many schools can truly answer with confidence and evidence? The more I thought about it, the more I realised that unpacking this question is no different to any other data dive we might do. It requires us to understand what we’re measuring, to find a range of data to analyse, and then to use all that evidence to gain a deeper and more holistic understanding of our current situation and future goals.    Step 1: Translating values into visible behaviours What does our mission look like in action? When we are living our mission, our values align with our actions. Let’s take “lifelong learning” as an example phrase we often see in mission statements. Schools that value lifelong learning will likely have administrators that promote and encourage staff professional development...

The End of Year Data Handover

“And then she went to the porridge of the Little Wee Bear, and tasted it, and that was neither too hot nor too cold, but just right, and she liked it so well that she ate it all up, every bit!” — Goldilocks and the Three Bears    For many of us, the sun is shining and we are in the mad dash of wrapping things up before escaping for a well deserved summer. I suspect it would be an easy task if wrapping up was all we had to do, but of course it’s never that simple in a school. We don’t just pack up our class, we need to hand them over to next year’s division and teachers, and get ready to receive our next batch. The data handover is enormous, and figuring out the what and how requires a “Goldilocks” attitude - we’ve got to get it just right. Quantity: Sharing enough to inform, but not so much that it will overwhelm next year’s teachers.  Ingredients: Not sharing just numbers, but also anecdotal records and holistic data that will help teachers know students better and so...

Growing Data Champions

In my work with schools, I’m always on the look out for a school’s data champions: the early adopters of a culture where data is valued and is used to improve schools and student outcomes. Data champions help colleagues understand how to find, interpret, and use data effectively. They are also translators, able to turn complex findings into clear and actionable insights.  Image by  Mohamed Hassan  form  PxHere  - CC0 Public Domain We often go looking for data champions in the IT office, or failing that, in the math department, but the truth is that data champions are hiding in plain sight everywhere; anyone who believes in using data to inform choices, and who can convince others of the value of data, has the potential to become a champion.  So how do we find and grow these “sleeping champions”? Jim Collins share strategies for building “enduring greatness by cultivating a talent pipeline”. In a data context, this could include: Modeling data-driven d...