It’s no secret that big data is changing the economic landscape. But professionals who use big data to make decisions, solve problems, and investigate complex issues are not working only in fields like computer science and technology. Increasingly, big data specialists can be found in fields such as law enforcement, marketing, and the humanities.
However, Ruth Krumhansl, director of the Oceans of Data Institute at EDC, thinks that K–16 instruction is not keeping pace. “We need to raise awareness about the importance of preparing students for the jobs and lives ahead of them,” she says.
Q: Why do we need to change how we teach data analysis and data literacy?
Krumhansl: The skills, knowledge, and behaviors needed to make sense of large data sets are becoming increasingly essential, but schools and colleges have been slow to adapt to this reality. This has real economic implications: we know that many students are graduating high school, college, even graduate school, without the skills needed to enter many of the data-heavy jobs that are available. And there’s a societal importance, too. Students will be graduating into a world where big data is an essential part of nearly every part of their lives, and they will need the analytical and intellectual skills to make informed decisions.
Q: What skills do students need to be successful users of big data?
Krumhansl: Students must be able to make meaning of what we call “CLIP” data sets. These are data sets that are complex (containing a variety of data collected different ways), large (containing more than just the data needed to answer any one particular question), interactively accessed (so that students are able to create their own data visualizations and compare different data sets to look for relationships), and professionally collected (collected remotely, by someone else, requiring students to learn about the data collection instruments and methods).
To explore CLIP data in productive ways, students should be doing activities that help them recognize potential patterns and relationships, ask critical questions about what these might mean, and then dig deeper into the data to pursue the answers to these questions.
Q: How can a teacher integrate more rigorous data-related activities into instruction?
Krumhansl: Although traditional classroom work with small, student-collected data sets teaches important foundational skills, it’s no longer enough to prepare students for a data-infused world. The good news is that there is unprecedented access to CLIP data online. For example, weather, climate, and astrophysical data from NOAA and NASA; earthquake data from organizations such as IRIS and demographic data from the U.S. Census are all now available. Teachers can help students learn about the variety of instruments used to collect these data. They can also help them become familiar with a variety of data visualizations and teach them how to think about the kinds of questions the data can answer. Perhaps most importantly, students can practice using analytical tools to explore these data on their own. It is both challenging and exciting work.
Q: Why are these skills important to students who pursue careers outside of big data fields?
Krumhansl: More and more non-technology jobs require an ability to use and make sense of data. Take teaching. Teachers are now expected to gather, analyze, and interpret data about their students in a way that simply was not the case a generation ago. Farmers are using new kinds of sensors to understand the right time to fertilize and water their crops. Data skills are increasingly critical to the day-to-day jobs of car mechanics, real estate agents, and health professionals, too. And all of us are inundated with data in the media every day. So even if students do not go into a technical field like analytics or computer science, they will still be expected to make data-based decisions. It’s an essential skill.