Welcome to #BeyondTheMean! Check out this post to see what this blog is all about.
I’ve recently finished a month-long conference circuit that has given me the opportunity to talk about evidence-informed continuous improvement with hundreds of educators across the nation. I always value the opportunity to spend time in the world with classroom educators who help me think about my work in a new way. These conversations have led me to adopt a new common refrain: Work with data that works for you!
A common theme from my month on the road is the feeling that data analysis for continuous improvement is incongruous with the values of education. Many folks I have talked with expressed warranted frustrations about standardized testing, the sometimes-unclear nature of data collection requirements, and the feeling that data analysis interferes with the art of teaching.
I get that! When I was a classroom teacher, data analysis tasks always felt like a waste of my time. What I didn’t understand back then was that my data analysis tasks felt cumbersome because they didn’t place value on my local classroom context. As the only music teacher in my building, spending time with standardized reading and math data didn’t feel like a meaningful activity.
Since leaving the classroom and embarking on my journey to elevate evidence-informed continuous improvement I have found that many teachers waste a lot of time on data analysis that isn’t meaningful to them. They are looking at data they way they think they are supposed to be looking at data. When data analysis is not meaningful to the practitioners at hand, it is simply not a good use of the classroom teacher’s limited time.
Now – I’m not here to tell you that classroom teachers shouldn’t be using data. Rather, we must take steps to help educators work with data that works for them! The challenge is not “how do we remove the data analysis burden from teachers”. The challenge is “how do we remove the burden from a teacher’s data analysis.”
So, what does it mean to work with data that works for you? It starts with individualizing the data analysis process. Rather than loading teachers up into the library to participate in analysis discussions led by the school’s administration, we should focus on supporting teachers as they identify locally sourced data elements and develop classroom level protocols that are meaningful to each individual classroom teacher. What works for the sixth-grade math teacher may not work for the eighth-grade social studies teacher; and that is okay! We say this about our students all the time. I think its time that we apply that mentality to our teachers as well.
Part of the individualization process is gaining a new definition of what “data” are. When I say the word data in front of large audiences, I inevitably hear groans and occasional shouts about the invalid or immoral nature of standardized testing. While I am in favor of some standardized testing in some circumstances (see my post Standardized Testing is Not a Monolith), I don’t think it is a productive use of time to require classroom educators who don’t believe in standardized testing to spend hours analyzing their results.
Teachers collect data all day every day. Sometimes they write it down, other times they process and adapt to the data inputs in real time. No teacher will tell you that the data they collect through their own instruments and processes are invalid or immoral. By broadening our thinking about what data are, we can help teachers learn to leverage a wide range of data for more meaningful data use. As educators at every level learn to leverage data in a meaningful way, they will add new data points into the mix on their own as the value of different types of data becomes clearer to them.
One good example of “alternative” data are the many family level data points that we collect in our schools. Consider phone numbers and addresses. The frequency with which a phone number or address changes for a student is a remarkable measure of economic, social, or societal instability. When a kiddo brings you a new address or phone number, do you consider that a data point? Would you agree that a student who has changed addresses six times in two years has more outside-of-school challenges than a student who has had the same address or phone number since they entered the school system? This is an excellent data point that can help teachers predict educational risk.
As we work to help teachers find and work with data that works for them, we must also provide them with the skills they need to do the work successfully. In our efforts to remove the burden of data analysis, we have transitioned to a space where we rely heavily on third-party tools and contracts to help do the data analysis for us. Schools spend millions of dollars on software packages that take data in and produce tidy charts and graphs. The problem with this is that those charts and graphs don’t always tell the whole story and when teachers have deeper questions about the outputs, they don’t have access to the raw data to dig deeper.
I am a big proponent of the exploratory data analysis (EDA) technique. EDA is an iterative and open-ended process of examining data with a curious mind. It focuses on replicable processes over complex theory and can usually be performed successfully with only the most essential spreadsheet skills. Most of the skills I wrote about in my book, Exploratory Data Analysis in the Classroom, are fourth grade math standards in my home state of Kentucky.
EDA is an amazing continuous improvement construct because it allows you to see the answers to questions that you never thought to ask. Since it focuses on real world skills, teachers can dig deeper into the data and truly understand the anomalies that they see. EDA also requires practitioner knowledge to make sense of the data; encouraging classroom teachers to think and engage in the data analysis process in a way that they haven’t been able to before.
When we empower classroom teachers to collect, analyze, and engage in data analysis processes that are meaningful to their individual classroom context, we empower them to make intentional decisions that lead to lasting change in teaching and learning. By focusing on skills, we can eliminate the burden from the data analysis process and help teachers work with data more efficiently. When teachers are empowered to make better instructional decisions, we can accelerate student learning and promote long-lasting school improvement efforts.
This transition is not easy. It requires a change in mindset, a dedicated effort to skill building, and the adoption of systemic data collection practices. It will take a bit of extra work up front, but once the skills and systems are in place, individualized data analysis techniques will save time, lead to better decision making, and higher staff morale. As you get started, check out the free resources I have in The Repository. My auto-analysis tools can help you process data more quickly and my video series and eBooks can help your teachers acquire the skills they need to implement the EDA technique in their own way. Good luck on your journey friends and let me know how I can help.