How collecting warm data can tell us more about our students

We’re likely to miss the root causes of drops in test scores and other issues because we don’t often collect 'warm data' about how our students are feeling.
Victoria Curry
Victoria Curryhttps://www.ulsterboces.org/
Victoria Curry is the instructional services supervisor of school development at the Ulster Board of Cooperative Education Services in New York. She has been a school and district leader in public education for more than 19 years. Experiences such as assistant principal, classroom teacher, technology integrator, district special education liaison, and professional development specialist have provided Victoria incredible insight on how to best support teaching and learning. Current areas of interest and speciality include data visualization, humanization of data analytics, and inspiring educators to take risks and fall in love with the process of teaching and learning. She can be reached at [email protected].

In education, we don’t have a lack of data. In fact, sometimes we have so much that it can be overwhelming, particularly for educators who aren’t naturally inclined to look at numbers and make sense of them.

The data that we have also tends to be somewhat dehumanized. We can see, for example, that reading scores are dropping, but if that’s occurring because students are anxious about something happening in their world, we’re likely to miss the root cause because we don’t often collect data about how our students are feeling.

To address this issue, my colleague Mike Setaro, principal at Kent Elementary School in New York, and I began using what we refer to as “warm data,” which is simply social and emotional data. Here’s a brief explanation of how I use warm and cold data, and how collecting humanizing data can help districts better serve their students.

Putting data to work

No matter what kind of data you’re working with, sticking to a protocol is key. It can actually be harder for well-established teams to glean insights from data because they have so much history together. It can be so easy for people to see their own anxieties, goals, and needs in data instead of understanding the story that’s actually there.

While it’s important to understand how people are feeling, projecting our own feelings onto the data can make that more difficult. Having a protocol and sticking to it allows the data to tell its own story.

The other pitfall that people experience is that, after collecting the data, wrangling the data, and talking about the data in team meetings, nothing changes. Data work is systems change work, so it’s important to be focused on an action-oriented end goal. I love a good spreadsheet but spreadsheets do not make change.

I rely heavily on the Data Wise protocols from Havard University as a guide. I like to categorize data into short-, medium-, long-, and extra-long-term data to inform different kinds of decisions.

I also like to visualize data, which is important for two reasons. First, it forces me to get all my ducks in a row. Reading inventory, math benchmarks, and social-emotional learning data, among others, all come from different places, but if I’m putting them into a series of graphs, I have to bring it all together. This is something many schools and districts really struggle with.

The second important thing about visualization is that I can then overlay demographic data on top of it. If I have all my algebra scores throughout the year, I can look at how the girls are doing or the younger boys in the cohort.


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Finally, I try to determine what important data I might be missing. Achievement data for high school students, for example, is really easy to find. Ask any teacher and they can point you to students’ Regents Exams scores or classroom test outcomes. But if I’m trying to do a root-cause analysis and I don’t have demographic data, for example, I might not notice that all the girls with falling math scores are on the volleyball team. Or there might be a problem with the human flow through the school that is causing all the students coming from science class to be late for math

All of the data I’ve mentioned so far is cold data. Demographics, graduation data, attendance rates, suspension rates, and achievement metrics are all cold data. They can tell us a lot, but they can never tell us how people are feeling and people’s feelings—be they students or educators—drastically affect how students learn.

Warm data in practice

Gathering warm data is as straightforward as asking, “How are you doing?” when, for example, implementing a new math program. Principal Setaro and I like to use Marc Brackett’s Yale Mood Meter to ask this question. For administrators who are implementing this, I suggest they train the whole district, both students from pre-K to 12 and the adults, on using it.

Together, warm and cold data can give us a holistic view of a student. For example, if we look only at the cold data of a student who has a 90% attendance rate and a 92 achievement score in algebra, we’ll think that everything is going great with her. But maybe if we ask her how she’s feeling she will say, “I hate algebra. I just want to read books, but I’m trying to get into a particular college and it brings so much stress into my life.”

Asking that student how she is feeling teaches her metacognition, and it may help a teacher who previously thought everything was going well to see that burnout is on the horizon for that student.

Using warm data with faculty and staff can be helpful as well. When I was in a district doing equity work with an outside consulting agency, we collected anonymous data about how educators were feeling about the work. Then we used the collective responses to help the administrator determine when they should push the work forward more aggressively, and when they should pull back a bit to let everyone adjust to the changes around them.

Similarly, in a staff meeting, if attendees give an anonymous Mood Meter answer and a lot of them are coming in red, maybe the person leading the meeting can pull back on some of the things they were planning to discuss.

A lot of leaders will take these kinds of readings on people naturally, but at the district level or even at the school level, some leaders work with so many teams that they simply can’t keep on top of them all. Taking this kind of data over time can help them understand when they need to change their approach, if not their content. It can help them see subtle changes over time. If leaders are visualizing this data, they may see that the math team is doing just fine but the English language arts team has been shifting more into the red over the last term.

Maybe that isn’t a sign to pull back so much as an indication that the next ELA team meeting should begin with a little air-clearing. Why is everyone in this department getting unpleasantly worked up?

Using the Mood Meter is asking a simple question, but it can uncover complex issues, and it really brings home the understanding that people and their feelings come first.

Why warm data matters

Brackett, the creator of the Mood Meter, is a champion of the idea that we need to teach students the vocabulary to articulate how they’re feeling. Does “I’m mad” mean someone is a little annoyed or that they are furious? Training a school or district in this sort of vocabulary allows students and adults alike to distinguish between those emotions but it also gives everyone the training to be able to say, “It’s OK that you’re mad right now. I’m still here for you.”

Understanding emotions in that way is important in schools for two reasons. First, the adults in the building need to understand their own emotions. If I’m a teacher, I have to take care of myself first to fully be there for my students. That begins with a self-check-in and, if I’m not happy, I need a tool to shift myself to where I need to be.

The second is that students need to be able to do the same thing. Are they happy? Sad? Angry? If they don’t like where they are, what can they do to change it? This is where the social and emotional learning that many schools are implementing can come together with warm data—to put students in the driver’s seat a bit more by providing tools to help them move past emotions like frustration.

It’s important to check in with individual students, but it’s also helpful to check in with a whole class. Is there a cohort in the room that hates algebra? The only way to find out is to ask them how they feel about it.

Data is a four-letter word for a lot of educators. Teachers are caring people, though. They don’t go into education because they are eager to mark someone present or absent, but because they want to change lives. Teachers want to help children feel good, make good decisions, and become more independent, whether they’re in pre-K or 12th grade. Incorporating warm data helps give students autonomy and choice, but it also gives teachers a broader understanding of what data is. It can be a powerful entry point for those who are otherwise reluctant to work with data.

We are all here to do the job of teaching, but we are also part of a community that we need to build up. Ultimately, warm data allows educators to be more responsive to the community around them and make it the most nurturing space it can be.

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