We Must Integrate Data Literacy Into Teacher Prep, And It's Easier Than You Think

When my colleagues and I asked a group of pre-service teachers to graph and make a claim about data, the response we got surprised us.

“Data exploration is completely new to me,” said a college junior studying to become a K-6 teacher.

It immediately became clear we needed to back up a few steps. How can we expect our future teachers to teach their future students data skills they themselves never learned?

Despite pushes for change from industry and policymakers, data science education remains idiosyncratic and incomplete across the country. The result?  Many high school graduates lack the necessary skills to make sense of data. Less than 20% of high school students complete a statistics course, let alone any coursework across subjects dedicated to working with and analyzing data.

Adding to this, few teacher training programs include data literacy at all. If they do, their curriculum lacks the depth necessary for prospective teachers to teach their future students data skills. Which prompts the next thorny question: How can we expect our future teachers to teach their future students data skills they never learned themselves, nor learned how to teach?

Why Data Literacy Matters

Data is a critical 21st-century skill for citizens and the workforce. According to Udacity,  59% of employers report negative business impacts because employees lack data skills. Industry demand is beginning to drive states to require data skills in educational standards (e.g., NGSS, CCSS-M), in state graduation requirements (e.g., CA Data Science), and in teaching job descriptions. 

Policymakers are responding, too. In the fall, Senators Padilla, Booker, and others called on the Institute of Education Sciences and the National Science Foundation to focus more on enhancing equity and access to high-quality K-12 data science and literacy education.

Fortunately, there has been an increase in the number of data-based activities to hit the market in the past decade to help students “learn data” (e.g., free activities at CODAP, Data Nuggets, Using Data in the Classroom). Yet formal K-12 education systematically remains slow to respond.

Data Literacy Starts When Aspiring Teachers Use Data

As humans, we learn best by doing. Anyone who has taught—or tried to help someone learn from home in the past two years—can attest that knowing a skill yourself is different from knowing how to break it down to teach it to someone else. But without knowing the skill yourself, teaching it to another person becomes truly impossible.

We cannot expect someone to teach skills they themselves do not have. The first step, therefore, is to provide pre-service teachers opportunities to learn to use data themselves. Greater data literacy for students starts by developing the data skills of their future teachers. 

Next comes training in the pedagogy of data literacy. We train teachers how to teach subject content, classroom management, subject skills, etc. through our pre-service programs, but not data. Currently, we presume that pre-service teachers will intuit how to teach data literacy along the way. 

This approach is misguided. To set future teachers and their students up for success, we need to actively integrate data literacy skills and pedagogy into Pre-Service Teaching programs.

Teacher Prep Can–And Does–Include Data Literacy

We are starting to see progress at universities around the country; SUNY Fredonia and The College of New Jersey redesigned their teacher preparation science methods courses to include data science and data literacy over the past three years. 

Last year’s Data Science for Everyone Commitments Campaign spurred many teacher preparation programs to get on board. Associate Professor Victor Lee at Stanford Graduate School of Education committed to spearheading a pre-service teacher education course on teaching data science across disciplines. Georgia State University committed to producing and delivering self-paced, online courses that provide micro-credentials in data science to pre-service and in-service K-12 teachers. 

To be sure, incorporating such skills and pedagogy takes time in an already packed curriculum. 

Recent pilot studies suggest that teaching data pedagogy will not require programs to pack in an additional course. My colleagues and I have been researching how data literacy is being incorporated into existing STEM Methods courses. Our research indicates that pre-service teachers can gain confidence in their data skills and self-efficacy in teaching with data within a single semester (manuscript in prep). In other words, we do not need to add another course to get at this. 

It Takes Commitment, Not Another Course

In fact, I would go so far as to say we should not add a “data” course that separates data skills from content and pedagogy. Data is part of, not separate from, content instruction and effective teaching practices.

Creating such opportunities for our future teachers to practice data skills themselves and learn how to teach with data within existing courses means: 

1.  they are learning the skills
2.  they are learning how to teach the skills to future students, and 
3.  not taking more time from the curriculum, but instead, role modeling integrated data pedagogy. 

When we do this, pre-service teachers tell us how much they have grown. “Not only do I now have resources that I know how to use and incorporate into my own learning and future lessons, but I also have an overall better understanding and comprehension of data literacy and what that means in the classroom,” said a pre-service college sophomore at TCNJ on our pilot post-evaluation survey. 

If every STEM Methods faculty took on integrating data literacy skills and pedagogy as a component of their current course, we would no longer be asking our future teachers to teach their future students data skills they never learned themselves nor learned how to teach. 

Instead, we could quickly shift to setting our future teachers and students up for success in the data-driven world of the 21st-century. It doesn’t need to take a huge amount of time, but it does demand a commitment.