Data literacy is an important part of the sciences, but it’s an important skill our students should master even if they aren’t going to grow up to work in a STEM field. In society at large, data is used to argue or persuade — to vote for political agendas, to enroll in this or that school, or to purchase one car over another. Data collection is an important part of many fields — bakers may keep logs of their recipes, gardeners track their plant growth, and homeowners keep an eye on their electrical bill month to month. People are constantly making conclusions from data, and the ability to understand how data are manipulated and presented is an important part of being an informed member of society.
In the past, data literacy was presented as a part of the scientific method. (Side Note: If you haven’t yet, STOP TEACHING THE SCIENTIFIC METHOD. Check out the December 2018 workshop, “Stop Teaching The Scientific Method (And What To Do Instead)” if you still need more convincing.) “Cookie cutter” lab investigations were used to introduce the method, and oftentimes these were followed by similar cookie cutter science fair projects.
As educators realized these lab investigations were doing little to help students understand and practice science as a discipline, inquiry investigations were introduced. Unfortunately, many teachers (myself included, for many years!) were not given the guidance, instruction, or tools to effectively utilize inquiry to develop science skills and understanding. Data literacy is one tool that can help make inquiry-based learning more effective.
Activities To Develop Data Literacy
What I See, What It Means:
I’ve referenced this activity again and again, but it is one of my favorite approaches to analyzing data because it’s framework can be applied to so many different situations. “What I See, What It Means” is all about helping students make observations first and then interpret those observations to make meaning from the graph, map, or table. The two step-approach guides students through the process and leads to deeper analysis. If you haven’t already, you can download this free resource by joining my email list!
Data Analysis Sentence Starters
When given data (whether student-collected during an investigation or provided by the teacher), ask students to identify patterns they see in the data. To help scaffold this process, provide sentence starters. For example, “As the __________ increases, the ______ tended to…” or “In [these examples], we saw that changing _______ caused…” “I noticed a pattern in the __________. It…”
Sentence Strips: Drawing Conclusions From Data
To scaffold the process of drawing conclusions from data, provide students with statements on sentence strips that describe patterns in the data (both accurate and inaccurate statements). Discuss which statements are accurate and which are not. Ask students to use the data to prove or disprove the statement.
Comparing Visual Data
Ask students to graph data (allowing them to choose the type of graph they will use). Provide basic checklists so that their graphs contain key components (titles, axis labels, etc). Then, have students compare their graphs by conducting a gallery walk. Ask students to write two positive statements and one critique of each visual representation. Discuss which graph best represents the data (most accurately, most clearly) and best supports an accurate interpretation of the data. Identify which graphs present a skewed interpretation of the data and discuss how they could be improved. *This activity requires a degree of trust that may take time to develop in a classroom. Incorporating opportunities for students to give and receive constructive feedback in low-risk activities can help build this in your classroom environment.
Incorporate Real World Data
As I’ve already said, whenever you can, incorporate real-world data into your classroom. There are so many databases online today that track real-world data, there’s no reason you can’t find some to bring right into your classroom!