What are good strategies for students analyzing the data and connecting it to other content? (Process for Teaching with Data, Phase 4)

What are good strategies for students analyzing the data and connecting it to other content?

Finally, making sense of the data for what it means and how it relates to the content you are teaching. In other words, often the whole reason for why we started to integrate data into your curriculum in the first place.

But unfortunately, our student responses to our final questions or the claims they put forward in the Claims-Evidence-Reasoning framework often fall short of our hopes.

What can we do about it? Change how we are teaching our students to complete this final crucial step of working with data.

 Similar with other steps in helping our students work better with data (see our other resources here), we need to carefully think about how we frame our prompting questions for our students as well as how we ask them.

First let’s think about effective prompting questions:

  • What does the pattern (that you previously identified in the data) mean?
    • Knowing what you know about the subject, do you think this pattern is meaningful or not?
    • Knowing what you know about the subject, do you think this pattern is unusual or is it what you would have expected?
  • Why do you think this pattern exists in the data?
    • What could be causing (or as we say driving) the pattern?
    • How could it be that this pattern exists in these data?
  • How sure are you in the pattern?
    • Is it really obvious or did you have to search for it?
    • Are the data values all tightly together or are they really spread apart?
    • Why does it matter how sure you are in the pattern for us deciding on our conclusion from the data?
  • What does this all mean to you?
    • What does it make you think of about the subject now that you have looked at these data?
    • Have you confirmed what you expected? Have you found something that doesn’t make sense from what you thought you would see?
  • How does this pattern in these data relate to something else that you know?
    • Is it similar or different from what you knew before?
    • Was it surprising or nothing new to add this information to what you already knew about the subject?
  • What have you learned from these data?
    • If you were to explain what you learned to a younger sibling or friend, what would you tell them you learned about the overall subject from these data?
    • What about what you learned about this way of looking at the data?

Now let’s think about how we are asking our students these questions. As this is a key step to understanding how the data connect to other aspects of your curriculum, make sure you are providing opportunities for students to build these skills over time and supported by you before you ask them to do it by themselves. Some ways to support this process include:

  • Verbally role model your process as you make sense of the data (especially if it is in a new content unit or from a new kind of visualization)
  • Have them analyze the data with a partner and then verbally explain their analysis to a new classmate
  • Have them write down their initial thoughts, then work with a partner or in small groups to verbally share their thoughts, and then provide them time to revise their conclusions
  • Have them post their thoughts anonymously in a gallery walk setting so that they can provide questions and suggestions to one another using post it notes, then provide them with additional time to revise their conclusions.

Regardless of how you provide the space for students to develop these skills, the important thing is that you provide multiple opportunities in different ways for them to practice analyzing the data to make sense of it.

Remember data literacy is a learned skill that we gain through practice, just like reading (literacy) and doing math (numeracy) we have to break it down into its smaller components to help our students build up to the bigger more complex workings with data.

Over time the students’ abilities to analyze the data themselves will increase and you will be pleasantly surprised with the responses you get to your prompting questions. And then it will be a lot more fun using data in your teaching!

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