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A Positive Approach to Analyzing Data

By:  Cailin Minor, The Columbus School, Medellin, Colombia


Anyone who works in a school knows that analyzing data can be a springboard that launches educators into meaningful action and positive change in student learning. We also know that data can be a four letter word that can launch people into uneasiness, defensiveness, and even dread. As a Literacy Coach at The Columbus School in Medellín, Colombia, I have seen both of these scenarios play out. I have seen the power of teachers coming together as a team to analyze data, make meaningful reflections, and take action in the classroom. I have felt that positive energy that comes from knowing you are doing something that will have an impact on your students. I have also seen data reflections turn into a downward spiral of negativity, blame, and an overall lack of creativity which hinders us from creating change. Through research and continued practice, I have been experimenting with a new method of data analysis to help teachers tap into their creativity, access their internal resources, and feel confident about taking action with their students.

The Data Driven Dialogue

At TCS we have begun using the Data Driven Dialogue protocol to guide our data conversations (School Reform Initiative). The protocol goes through the following phases:

Phase 1: Predictions – Make predictions of what the data will show Phase II: Go Visual- Data is presented in a clear and visual way for educators to study Phase III: Observations- State only what they notice, no inferences Phase IV: Inferences and Implications Analyze factors contributing to data through answering reflective questions Implications for teaching and learning, make next steps and action plans

As a facilitator of data dialogues, I noticed that the most crucial part is where teachers analyze the causal factors of the data and make inferences. As a facilitator I was asking reflective questions and pushing the teachers to get to the root causes of what was going on with the students. Although we would try to focus on both the students who were meeting expectations and the ones who were not, the conversation was always dominated by the students who were not successful. We would spend most of the time analyzing those students and trying to solve the problem of what was not working and how we could get them to grow. The implications of “What are we not doing or not doing well enough? What are we missing? Whose fault is it?” would hang over the conversations like a dark cloud. By the time we moved on to action steps you could tell the well of ideas and solutions were pretty dry. The tone at the end of the meeting suggested that some teachers didn’t feel confident enough to take the next steps and make changes.

I think many educators working with data have found themselves in similar situations. We set out to analyze data with the intention of getting to the bottom of what’s keeping our students from achieving mastery and finding solutions to support them, but get lost along the way. We get bogged down in thinking about all the things that are “going wrong” and what seems like a laundry list of things that students are struggling with. What if there was another way to analyze data with the goal of supporting students who aren’t learning, without analyzing students who are not learning?

Appreciative Inquiry

If we look to find a way to modify how we analyze data, the practices and beliefs of Appreciative Inquiry (https://appreciativeinquiry.case.edu/) can serve as a great tool. When we think about how we can create change that results in students meeting expectations, we often get trapped in the “Find a Problem, Analyze the Problem, Create a Solution” loop. This encourages focusing on what is wrong or broken. The best case scenario is that we find a solution; the worst is we set off that downward spiral that can lead to negative feelings surrounding data analysis and low efficacy for supporting our students long term.

The principles of Appreciative Inquiry seek to flip our practices surrounding fostering change and student achievement. Instead of analyzing the problems, we analyze the successes. Instead of looking at the gaps and weaknesses, we look at what is going well. Instead of asking questions that focus on what’s going wrong, we ask questions that help us figure out our strengths and how we can replicate that for other students. This approach supports the idea that the best way to get the positive change you are looking for is to find the successes, figure out what you’re doing right, and find out how you can get more of that. It encourages educators to feel confident about what they are doing, think positively, imagine possibilities, and take risks to create the change they want to see.

A Positive Approach to Data Analysis

After learning about Appreciative Inquiry, I applied the principles and mindset to the Data Driven Dialogue. I decided that during the Inferences Phase, where the teachers analyze the causes of the data, we would only focus on student successes. Our goal was still to use the data to make action steps to support students who weren’t learning, but how we planned on reaching that goal changed. We were now approaching it by analyzing our success stories and applying what we learned to other students.

As I explained the modification to teachers, I could see the questioning look. Many seemed reluctant about not putting the focus of our discussion on the kids who were behind, but I assured them that they would come into the discussion during the action steps phase. As we began, I would only ask positive reflecting questions like the ones listed below. We analyzed strategies that worked and decisions that teachers made that led to students learning. We also looked at other possible factors such as environment, motivation, and differentiation. As groups shared their positive stories, even normally quiet teachers became confident and more vocal. There was no blaming, negativity, or feelings of helplessness. After the analyzing and reflecting, I would summarize all the successes and things the teachers were doing that supported students learning. At this point I would shift the conversation by posing questions that asked teachers to apply the ideas from their reflection. The teachers at this point felt empowered to create solutions and action steps. They already had a bank of strategy ideas that worked for the students. It was nice to see teachers leaving data sessions having reflected deeply on the data and their practice, as well as walking away with action steps born out of their own team’s success stories.

The Modified Data Dialogue-Phase 1V Inferences & Implications


data

When we analyze data we don’t have to be trapped in the problem solving loop. We need to remember that data shows us what we and our students are doing well just as much as it shows who is not learning. Analyzing those successes is a practical way to approach data. Why would you study what’s going wrong to find answers when you have stories of student achievement right in front of you? By taking a positive approach to data analysis we can empower teachers to unlock the true potential of data and its impact on student learning.

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