In today’s information age, we all want to claim we’re data-literate, but what does that really mean?
Does it mean we can look at data and understand it? That we use patterns in the data to guess what happens next? That we know how to collect it in the first place?
Grappling with data most likely requires a combination of all these elements, and it’s something many individuals will have to do in their careers.
That is why teaching data analysis and making sure students are data-literate from an early age is so critical.
Being data-literate allows you to look at the world through numbers to understand it better, but it’s also a means to evaluate it.
It’s a method for making informed decisions whether you’re looking at a societal problem, a professional puzzle, or even a personal question.
What many used to call statistical thinking has now morphed into data literacy and encompasses the value data has in our everyday lives.
It’s how we can make sense of the world, prepare for the future, and come to well-researched decisions.
From an educational perspective, data literacy empowers students to use critical thinking skills and make connections beyond a single discipline.
Teaching data literacy fosters skills in math, statistics, quantitative reasoning, and more.
It’s also something you, as an educator, can use to inform your own classroom results.
Collecting student data, whether test scores, attendance records, or behavioral results, allows you to modify your curriculum and teaching strategies to achieve stronger student outcomes.
For example, if you review test scores and see that, overall, the class scored three points lower on the first multiplication test than the second, you may modify your curriculum to include more review.
Teaching effective data analysis to make one data literate gives students a skill set that stretches across all boundaries.
It’s inclusive of every student because it can be an essential component for anyone’s future. It’s something students can truly use throughout school and into careers in both STEM and non-STEM fields.
It’s also something many students begin doing automatically at a young age, even if they don’t realize it.
The first time they find a pattern in any information presented to them, they’re learning data literacy.
This skill advances as they get older and are asked to review charts of information or look at survey results from a classroom poll.
Teaching data analysis continues, extending beyond numbers to use critical thinking and evaluate the accuracy of facts. This is especially true in our current era of misinformation.
Whether students are collecting, managing, and reviewing data using pencil and paper or working with more advanced software to analyze and draw conclusions— they’re learning what it means to be data literate.
Data literacy may feel like a complex topic to cover, but by breaking it down, you can parse it out into applicable sections that students can practice and learn.
To start, make sure students understand how to ask a statistical question that leads to a need to investigate data.
Questions should be broad enough that they’ll need to collect multiple answers rather than a single, specific one.
So, instead of a student asking the person sitting next to them, “How old are you?” the student would need to ask, “How old is everyone in the class?”
Next, students must learn how to collect data relevant to their questions.
They need to learn to distinguish between information to get exactly what they want and how to sift through large amounts of data to hone in on what’s important.
For example, if in getting age data from the class, the student also receives information on the date of birth for everyone, they’ll need to know what to look at for the quickest results.
Analyzing the data comes next, and students should learn to look for patterns to develop models for showing results.
You should also ask them to visually represent information in different ways so they can see the data differently to understand it better.
At this point, students should also feel confident predicting the data or a future outcome.
Going back to the age question, if the collected data shows that 87 percent of the class is 12, and you ask the student to guess how old a new student would be who joined the class, they should guess 12, too, since that’s the more likely result.
The ability to answer questions and display their results wrap up the assignment, whether representing the data for the class, answering specific questions, or making predictions about what’s likely or unlikely to happen next.
No matter the initial topic, instructional data activities that incorporate all of these elements give students the practice they need to become data-literate.
As you begin to think about supplementing your current curriculum with some data literacy activities, consider these tips for improved success:
It’s also a good idea to visually and editorially represent activities like this, using illustrated examples so that all types of learners understand what you’re asking them to do.
And, as with any new type of activity, infuse the lesson with a lot of positive reinforcement as students use trial-and-error to figure out what it really means to collect and analyze data.
Encouraging data literacy in education is so important because of its lasting impact on all students.
Flexing this muscle and developing this skill helps students learn about and make sense of their world, whether in the classroom or at the office.
They’ll carry this ability with them for the rest of their lives, most likely using it more often than they ever anticipated.
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