Atlas Protocol - Looking at data

The Atlas Protocol is one of the most commonly used structured dialogue formats among schools to facilitate a conversation about data for teachers and other members of the faculty. Learning from Data is a tool to guide groups of teachers discovering what students, educators, and the public understands and how they are thinking. The tool, developed by Eric Buchovecky, is based in part on the work of the Leadership for Urban Mathematics Project and of the Assessment Communities of Teachers Project. The tool also draws on the work of Steve Seidel and Evangeline Harris-Stefanakis of Project Zero at Harvard University. The protocol gives a detailed step by step guide on how to prepare and conduct a healthy, productive conversation about and with use of data. It starts with a selection of datasets that do not lead to a single conclusion and generally lead to rich conversations. From that point forward, the protocol describes 6 stages the group has to follow with the help of a facilitator in a prescribed timeline. You can find all the details on our LAC Learning Center under ‘Data Conversations’.

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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...

Which comes first? The process or the culture?

“Should we meet with primary and secondary leadership teams separately or together?” This question came up during a recent chat with Joe Barder, IT Director at AIS Lagos, when we were figuring out a strategy for building their data culture. A simple question, but it got me thinking a lot about the difference between organizational culture and organizational practices, and how we need to consider both when fostering change. At AIS Lagos, everyone is eager to dive in and start analysing data. However I also sensed some hesitation from Joe about jumping in without first establishing norms and shared practices. The question of whether to have data discussions at a whole school or division level is really about whether we want to start with targeted, relevant, and actionable sessions tailored for each group, or if we take the time to develop a whole-school shared understanding of what it means to be data-informed. In other words, do we focus on the culture or the practice? On on...

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...

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