Old Town High School students returned to entering the tap water data they collected this fall after the holiday break. Early last week, we pulled the data into Tuva for analysis and saw that the students are building a rich picture of tap water quality around Old Town. These data are available to all Crowd the Tap Maine (CTTM) teachers. This post is the first of a series that describes how teachers can use these data, along with data that their own classes collect, to help students gain familiarity with some of the big ideas at the center of data literacy.
Background and Purpose
Some readers know about Crowd the Tap Maine. Heck, some are using it in their classrooms. But other readers are new to CTTM. So, briefly …
Like other citizen science projects in schools, CTTM aims at two goals:
- Supporting science learning
- Answering questions and addressing problems in the world
outside the classroom.
This series of posts focuses on the first goal: science learning for students. But the second goal is still part of the picture. We will share the data that the students are collecting with a nation-wide Crowd the Tap project that is looking at drinking water quality across the U.S. We also anticipate that as students collect more data, semester after semester and year after year, they will develop a map of tap water quality in their communities that will be useful for others.
For a broader picture of the kinds of data that students are collecting and the questions they can explore with these data, see our earlier post titled, “A Look at the First CTTM Data and What We Can Do With It.”
Where Would You Expect to Find Iron in Tap Water?
Students contributing to CTTM collect data from many different kinds of houses and buildings. Some are old; some are new. Some are stand-alone homes; some are apartments. CTTM provides opportunities for students to think about patterns. Do they see differences in the water that match up with different kinds of houses and systems?
This sample includes houses on Old Town’s municipal water system and also houses on private wells. Would you expect to find more iron in the water from wells or the municipal system? It is important to get students thinking about questions like this one before they look at the data. Asking in advance allows them to think about WHY there might be more iron in one kind of system than another. Giving them time to develop their ideas, maybe through think-pair-share or small group work, prepares them to continue thinking once they see the data — as opposed to just saying “Oh, OK” and moving on.
The Tuva data analysis tool that students use enables them to look at data on a map. Below is a map of the different iron levels that students found in the tap water that they sampled. It is a larger version of the map at the top of this post, showing more of the houses surrounding Old Town. Clicking on the image will bring up an even larger version.
What do you see here? How would you describe the geographical distribution of iron in tap water?
The map below shows the distribution of water sources that supply the water that students sampled. Again, clicking on the map opens a larger version.
Asking students to think together about the relationship between these two maps should lead to some discussion. Often, students will focus on particular bits of data rather than the overall pattern. For example, they might note that one of the two sites with high iron levels appears to match one of the public buildings. If they use Tuva to investigate more closely, they will find that, yes, that is a match.
That is an interesting observation about one building and perhaps one that might warrant a closer look. But it does not help answer the question: “Would you expect to find more iron in the water from wells or from the municipal system?” Sometimes students might feel uncomfortable answering a question such as this one because the data do not support a clear, certain answer. That is the case here. The uncertainly should be acknowledged; recognizing it is a good thing! (And, in terms of data literacy, this is an important step beyond being certain when one shouldn’t be.) But most real-world decisions are made in the midst of at least some uncertainty.
For example, suppose that we want to develop an even better picture of iron contamination in tap water around Old Town and have the resources to invest in a limited number of more expensive, accurate tests. Of course, we would want to use some of the more accurate tests on both municipal and well water systems. But we already have preliminary data that might help us decide where to focus our limited testing capacity to get the best possible picture of problem sites. Do the data we already have give us evidence to support focusing more on one type of water system rather than dividing resources equally across all systems? If we decide to divide the resources unequally, what is the best way to divide them up?
Notice that this approach takes the initial question and turns it into a new problem and new questions. It gives students an example of what it means to dig into data more deeply and why we do it. The next post in this series illustrates how pursuing these new questions provides opportunities to learn more about backing up assertions with evidence, thinking in terms of probabilities, and acknowledging and addressing uncertainty.
— Bill Zoellick
This method I really like. The resulting conversations could move in so many directions and just beg students to use charts and tables to support their thinking, questions, and subsequent claims. Yes, I think uncertainty is good, and I still struggle so much convincing my students that it’s where the real learning and magic happens. They so much want the binary correct/incorrect response to their typical math problem sets – and uncertainty is where my students tend to fall apart on labs.
I think that that asking them to continue generating questions to ask is vital too. When I look at the first map, I see that most of the yellow dots are clustered. I initially wanted to know more about them. Molly always suggested asking students to generate as many questions as they could about a table or chart – and to then challenge them to find the answers within the data, if they could. Maybe as instructors we should be regularly asking them how certain they are about their claims, rather than asking ‘why’ they’ve made their claims. While digging in their brains for why they are or aren’t certain, aren’t they likely to seek something tangible? Would that be our ‘in’ to the next series of questions to ask them?