When I taught an intensive literacy class for 10th graders I decided, somewhat randomly, to do a project based unit focused on data and data literacy. At the time, it just seemed like a good idea. Students had to research a controversial issue (the drinking age) and determine their position, based on data. We dove into a variety of studies (we started with procon.org– if you haven’t checked this site out, do) and as students did so, I became more and more glad that I had decided to do the project. Students had to reconcile conflicting information on the relationship between drinking age and driving fatalities, for example, and negotiate difficult charts. And it was surprisingly tough for them, especially initially.
When social studies, math, and science teachers ask about bringing literacy into their classes in a meaningful way, data literacy is something I always mention. And when ELA teachers ask about information literacy, I also mention data literacy. That’s why I’m so excited about New Tech Network’s Global Happiness Project. In this project, students will learn about what makes people happy and analyze a variety of happiness indices. They’ll propose questions for a happiness survey, then administer the survey to their communities. Finally, they’ll analyze the data they receive and create an action plan for increasing happiness in their school, community, or the world. The project lends itself to the meaningful exploration of lots of data and drawing thoughtful conclusions based on that data and would be great in a variety of classes for a wide age range of students.
So, what do you do if you’ve decided to tackle the Global Happiness Project, or another project focused on making meaning of data? How do you help students grapple with the information coming their way? That’s a whole book in itself, of course, but here are some possibilities:
1) Model your own thinking as you look at data, a chart, or a graph.
2) Teach students the kind of careful, slow reading necessary to make sense of charts, graphs, and study findings. Small words like prepositions really matter, as do symbols and numbers.
3) Ask what’s there- and ask what’s missing. Ask students to focus on what they see, and then ask them about gaps and other factors that might be affecting the outcome.
4) Teach students the difference between causation and correlation.
5) Teach students to find measures of central tendency (mean, median, mode) and range and draw conclusions based on what they find.
6) Have students make different visual representations of data, such as histograms and stem and leaf plots.
Check out TuvaLab’s free data literacy resources (TuvaLab’s Global Happiness Project page) for thought provoking data sets and datablog tools that can help students visualize and think critically about data.
What do you do to help your students become data literate?