By: Kristin Hunter-Thomson
Help, how can I get them to understand it better?
We live in an age of data, and we are now being told from all sides that we need to integrate more data into our teaching. But if you are like me, your students just don’t even know where to start when working out how to make sense of the data in a visualization in front of them.
Fortunately, Andy Kirk in his 2016 Data Visualization: A Handbook for Data Driven Design put together a great road map to follow when thinking about breaking down to steps to understanding data. While his approach is outlined for people that are actively working in the data visualization design field, I think it is worth exploring it to think of what we can take into our teaching. [Note, influences from this approach appear in our Developing CER Capability Framework.]
Perceiving: What does it show?
This relates to actually being able to read the data visualization (chart). This is a step that many of us as data experts jump over forgetting that our students as data novices often forget to do it. We need to role model and they need to explicitly and repeatedly stop to ask themselves questions that get at things like:
- Can I, in general terms, describe what this chart is trying to show me through data?
- Can I get a sense of what the data represent?
- Can I say what the data representations (e.g., color, shapes, sizes) mean in this chart?
- Do I understand the connection between the data representations on the chart and their perceived values?
These are the kinds of things that data experts do subconsciously. Certainly, I am NOT saying ask your students those questions, but it is worth thinking about it if the questions we are asking them lead to their understanding of this or just a regurgitation of what is on the page without any deeper understanding of what it represents.
For example, how many times has a student written in that Time was on the x-axis but then did not talk about the pattern in the data as a change over time? What seems like a misunderstanding in their interpretation may in fact be a lack of understanding of what is actually on the page.
So, we need to make sure they first get what they are looking at, then we can get them to make the next step.
Interpreting: What does it mean?
This relates to our large desire as humans to make sense of things and find patterns (even if they are not actually there :)). Once we know what we are looking at we can start to try to make meaning from it. Kirk suggests that we ask ourselves questions like:
- Is it good to be big or better to be small in this context?
- What does it mean to go up or go down for these data?
- Is that relationship meaningful or not in this context?
Now these are questions that Kirk is putting forth in terms of what a data visualization designer needs to think about when making data visualizations. But, we as teachers helping our students make sense of data visualizations also need to think about how interpretation is a distinct and separate step from data analysis.
How someone makes sense, or interprets, data is influenced by their prior knowledge about the content/context and the kind of data visualization they are looking at, as well as their ability to use that information to make connections to what they know and what they are now looking at in the data.
In other words, this is the step where we make meaning of data points. When we realize that the data points represent values, relationships, patterns. But we have to stop and make sure we are taking the time to understand what the implications of those connections are.
More than just a statement of what the highest and lowest values are, we need to think about the fact that gives us the range of our data and what that range of data could mean for our interpretation of the patterns/relationships in the data.
So, once we know what we are looking at and know what it means as a stand alone. Now it is time to bring it all together.
Understanding: What does it mean to me?
While this may seem a bit personal for science, where we are so often taught to separate our emotions and personal connections to be unbiased and subjective, we learn best when we actively have a connection to what we are learning. This active connection can come from an actual personal experience with the data/context (which is a knock out of the park, ideal) BUT it can also come from just pausing to think “OK, what does this mean to me?”
This is in fact what we are after as science teachers. Right? We want our students to be able to use the data to think about how their new knowledge from the data makes a difference in what they already knew about the context before looking at the data. If not, why else are we having them look at the data?
So, ask them what it means to them. Help move them out of guessing what you want them to say and teach them how to think about what they take away from the data. Have them share these with each other in partners, small groups, or as a whole group. Build a culture of talking about different interpretations and understandings of the data. There is never just one conclusion or claim to draw from data, so let them share. You may be surprised how many take away what you did from the data, and how much they learn from one another as they role model data understanding together.
Interested in learning more about our Developing CER Capability Framework? Check it out!