A Toolkit for Educators to Explore Qualitative Data in Schools – Make Meaning of Qualitative Data

In the research process, we use five steps:

1. Identify the problem.
2. Define how you’re going to research the problem.
3. Collect data.
4. Analyze data.
5. Interpret the data.
6. Share your findings.

Analyzing and interpreting data are separate steps. Analyzing qualitative data entails organizing these data into themes related to the problem you are researching. There are many ways to approach qualitative data analysis. At research institutions like Child Trends, we have staff time dedicated to cleaning data and using software like Dedoose or Nvivo for analysis.

In a school setting, there are more time- and cost-effective ways to analyze data—for example, you can spend time reviewing your data to identify recurring patterns and ideas or leverage artificial intelligence (AI) tools like ChatGPT or Gemini to summarize findings. If you are interested in exploring AI tools to support your data analysis, you should approach AI use in a safe, responsible, and thoughtful way. Check out our blog to learn more about considerations for using AI to improve the classroom experience for teachers and students.

Let’s use our math engagement example to demonstrate analysis by reviewing your data and identifying patterns. Perhaps you want to learn about instructional practices to help students meet math standards, so you’ve collected data using an observation tool. To analyze these data, you will read all information in the tool and identify themes you see.

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In this filled out observation tool, we might identify themes such as “students are supported” and “identifying familiar components in problems.”

The step of interpreting the data comes after analysis and is focused on determining how the results of your data collection help you solve or address the problem you’re exploring. We call this making meaning of the data because the goal is to turn data into something meaningful and actionable to support your work.

Making meaning of data is most impactful when done with a group of people who have a stake in your research problem. In this case, it would be most helpful to involve math teachers themselves—especially those whose classes were observed. This section will walk you through an example of making meaning of data from the perspective of a person facilitating discussions to interpret the data.

Making meaning

Preparing for the meaning-making session

Before you facilitate a meaning-making session, you should complete the following vital steps:

• Complete data analysis.
• Determine the purpose of meaning-making: How will results be used?
• Choose an activity (or activities) to help achieve this purpose and work within the amount of time you have.
• Determine which themes you will share with your group, and how.

With these steps completed, you can create an agenda and share it with meaning-making session participants.

Hosting your meaning-making session

Now that you’re prepared to host your session, let’s discuss what a facilitated session could look like! We include an example agenda below and explain each component in detail.

Table: Example meaning-making session agenda

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Welcome, teambuilding, and norms

It’s important to start the meaning-making session in a way that welcomes everyone to the space and sets the tone for working hard and having fun together. If your group meets regularly, use your normal welcome routines. If this is a newer group, start out with a brief welcome, quick icebreaker, and some norms for the conversation. Here are some examples of norms you could share:

• Share when you feel comfortable and in the way you like.
• Hear what others have to say and be respectful of their thoughts and ideas, even when they are different from your own.
• Take ownership when the intent of what you say does not match the impact.
• Honor everyone’s unique experiences, perspectives, and backgrounds.
• Share time and space as equally as possible with all participants.

Data collection recap

Everyone you work with or invite to this session will have been involved in the project in different ways (e.g., thought partner on data collection plan, collaborator on data collection, interviewee). To ensure that everyone is on the same page as they begin to make meaning of the data, provide a debrief or reflection about what data were collected, when and how they were was collected, and why. This portion does not have to take much time, but a quick recap will help participants engage in the discussion productively.

Present themes

Before you share themes with the group, reiterate what you hope to do with the data and why you’ve asked them to help make meaning of it. Are you hoping to identify 3-5 topics you’ll do a deeper dive on in peer learning communities? Are you going to write a memo to your administrator sharing what you believe should be the focus of professional development next year to reach your goals? Whatever it is, let the group know.

Then, share the themes you’ve identified from your data analysis. If there are a lot of themes, you might have to get creative about how you share them so you don’t overwhelm the group with information. You could divide the group in half and focus on one half during one session and the other during a second session. If you break broader themes into smaller subthemes, you could share only the broad themes or only the subthemes—there are a lot of options! Whatever you choose, it is helpful to give some context in the form of a definition or example of what the themes mean, along with how you’ve named them.

Meaning-making activity

Now you’re ready to make meaning from your data! There are many ways to make meaning of themes and data, so it’s best to go back to your plans for results from the meaning-making session and make sure your activities will help get you there.

Meaning-making activity ideas:

• Have participants group similar themes together into buckets/categories, and then name the buckets. This can help you learn how participants interpret relationships between themes and what the commonalities are, in their own words.
• Have participants prioritize themes or buckets. This can help you learn what is most important to small groups of participants, and what you should focus on if there’s consensus between groups.
• Ask participants to go deeper on themes. Give each group five themes and ask them to reflect on them with specific questions like:
  • What does this theme mean to you? How do (or don’t) you incorporate it in your classroom?
  • What are instructional practices that could be used to support this theme?
  • What support do teachers need to do more of this theme?

You can also plan activities that build upon each other. For example, you could do all three of the meaning-making activities shared above—starting with the first, going to the second, and modifying the third to ask the group to reflect on their top 2-3 prioritized themes. Another approach is to have small groups do one activity, looking at how the other groups made meaning of the themes, and then asking them to reflect on the similarities and differences.

Because the example agenda provided here is for 60 minutes, there’s only enough time to do one activity. However, if you’re meeting for a longer period (e.g., 90 or 120 minutes), you can conduct multiple segments of activities. You can also spread these segments out across meetings if you have more than one meeting scheduled, or if the group meets regularly.

Debrief

By the end of your meaning-making session, participants will hopefully have reflected a fair amount on the topic for which you collected data and are hoping to change. Provide an opportunity for either each group or each person (depending on numbers and time) to share some of what they discussed, learned, or are curious about after engaging in the meaning-making activity. This task can be as simple as giving each group two minutes to share highlights of their discussion or asking each person to share a one-word response to a question like, “How are you feeling after doing that activity?”

Wrap-up and next steps

Even if you only have 30 seconds, it’s best practice to reiterate what you’re doing with the information from this session and state any follow-up items for you or other participants to complete.

Determining next steps after the meaning-making session

Once you’ve completed the session, put all information you gathered in one place. Ideally, you’ve ended up with information that will help you move forward—whether it’s to identify your focus, highlight in a write-up about the process and what you’ve learned, or identify goals for a future meaning-making session or data collection effort.

By making meaning in this way, with people who have lived experience with the topic you’re exploring, you should learn more about how they interpret the results of the data, what they prioritize, how practices can better support them, and how you can advance your work.