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Data-Driven Professional Development: What’s All the Buzz About?

24 Feb 2015 by Emily Doyle

Try googling “data-driven professional development.” That’s what I did this morning and it yielded over nine million results. But what’s all the buzz about? What kind of data is important to capture? And how can that data be used to drive professional development?

What’s all the buzz about?

Let’s start by defining what we mean by “data-driven.” I found a few definitions, but thought this one might be worth sharing: “Data-driven means that progress in an activity is compelled by data, rather than by intuition or personal experience.” This EdSurge report points to data-driven professional development as one remedy for traditional one-size-fits-all solutions that don’t work. And we see data-driven activities all over the place—not just in education. There’s a reason why politicians like to cite statistics when they’re trying to build momentum for a new initiative. Data provide numbers, and numbers are objective, concrete, and give us something to strive for.

What types of data are important to capture?

When it comes to early childhood education, there’s a lot of data collection going on. A quick perusal of the Quality Rating and Improvement Systems (QRIS) Compendium outlines systems using data-yielding assessments, including environmental ratings assessments and teacher interactions assessments. This article from Science asked whether QRIS ratings could predict children’s learning; and while safe environments are essential for quality programs, the article found that the measurement of classroom interactions give us the greatest indicator for children’s learning.

How can data be used to drive professional development?

So we know programs are collecting data and we know it is important to collect data that measures interactions. Now the question becomes: What do we do with it—and how can we use this information to drive real results?

When it comes to using CLASS data to inform professional development, Teachstone recommends empowering coaches to make strengths-based recommendations to teachers. For example, rather than choosing professional development resources geared to a teacher’s lowest scores, a strengths-based decision might involve focusing on a dimension where there is a mix of effectiveness. From a data angle, this might mean recommending professional development resources aimed at improving a score of 3, rather than jumping right into raising that low score of 1. We have lots of free resources, including our coaching e-book, to help coaches and leaders learn more about using data to support teacher improvement.

Of course, this is just one example of how coaches and leaders use data to drive meaningful decisions about professional development. How does your organization currently use data?

 


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