Digital technology is changing our world at an unprecedented pace. Are we equipping K-12 students with the skills and knowledge they need to keep up? That is the mission of Data Science For Everyone, a national initiative working to ensure that all students become data literate and have access to the resources and educators they need to build marketable data skills that will help advance their lives and careers in a new era. In this Q & A with 5 Questions, Data Science for Everyone director Zarek Drozda discusses the program’s goals for data science education and its progress.
What is Data Science For Everyone?
We should design the K-12 curriculum as if computers and AI exist. In short, that is the mission of Data Science 4 Everyone, a national education initiative incubated at the University of Chicago’s Center for RISC with a clear mission: to ensure that by the time students graduate high school, everyone is equipped to be data literate, no matter their zip code, identity, or background.
The initiative gained significant traction after being featured on the 2019 Freakonomics podcast episode “America’s Math Curriculum Doesn’t Add Up.” Since then, DS4E has evolved into a significant coalition, now comprising over 3,000 K-12 educational leaders, growing early pilot programs from one to nineteen states. We’ve also been a catalyst for developing more than 120 educational resources for teachers and students, been featured in several national media outlets, and even been invited to the White House.
Our role is multifaceted: we centralize and distribute teaching materials, facilitate professional development and community practice groups, and work on building awareness and partnerships across educational and industry sectors. Our aim is to drive forward the inclusion of data science in education through collaboration, talent development, and continuous improvement.
Why is it important?
Digital technologies are changing at an exponential scale and our education system needs to keep up with that change. Take the term “data,” for instance; many students immediately think of mobile data plans. It’s important to broaden their perspective, showing how data is the fuel behind artificial intelligence, how data can be crucial in addressing community-specific issues, or how data can allow students to dive into areas they’re passionate about – whether it’s examining the viral patterns of TikTok challenges, understanding the dynamics of NBA teams’ scores and historic records, or dissecting the implications of recent policies in their neighborhoods. Being data literate is no longer optional, it’s a prerequisite for navigating the job market of the future. Without these skills by the time they leave high school, students will find themselves at a significant disadvantage in the 21st-century workforce.
Data can allow students to dive into areas they're passionate about – whether it's examining the viral patterns of TikTok challenges, understanding the dynamics of NBA teams' scores and historic records, or dissecting the implications of recent policies in their neighborhoods.
What's Been the Biggest Surprise So Far?
Our K-12 education system is defined by 50 different state systems, over 13,000 school districts, and a variety of intermediary units. It is a patchwork quilt rather than a well-oiled machine chugging in unison, which allows localized innovation but limited responsiveness at a system-level. Now add an economy-shaping technology (or rather, several in a short period), and you have a perfect recipe for dramatically widening inequality. When we recently compared our progress in the U.S. compared to other countries in developing data science and AI literacy education, the clearest difference was coordination. While the first introductory data science programs were developed as early as 2013, the number of students actually benefiting is still far under 10 percent.
Compare that to efforts in Canada, Scotland, or China, who have trained tens of thousands of educators, made national commitments on the scale of $600 million dollars, or grew undergraduate programs for data science from 3 to 250 within two years, respectively. Speed is not necessarily good, but a coordinated strategy typically is. The United States often is first to innovate something ground-breaking, but increasingly struggles to follow through or bring everyone into the fold.
Where do you see Data Science For Everyone in five years?
Our vision is ambitious but clear: to spearhead the integration of data science into K-12 education nationwide by 2030. In this data-driven age, every student must graduate high school with a strong grasp of data literacy. This includes the ability to critically evaluate quantitative information, learn rigorous analysis theory and techniques, utilize fundamental data analysis tools, understand the ethical implications of data use, and communicate findings effectively.
With evolving AI technologies, students now need to know what AI can and can’t do, how it works, and why sometimes it doesn’t work the way we expect. We need to stay ahead of the curve by ensuring our students can confidently navigate through it, with the guidance of a qualified educator to do so rather than sticking our heads in the sand. Our goal is for data science to not only enhance traditional curricula but also to expand participation in STEM and computer science, cover critical questions in ethical use and consequences of data-driven technologies, and prepare students for a diverse range of careers and responsibilities of the modern world.
If we continue down the path of focusing only on the high-flying builders and engineers of artificial intelligence tools, we will produce one hundred Youngstowns for every one Silicon hub - a country that is not able to benefit or question the benefits of technology in the hands of a select few.
What Else Should People Know?
My family is from Youngstown, Ohio, often lamented as the poster-child of the rust-belt or even American economic decline. Through a very strange upbringing, I eventually landed in Silicon Valley, a community that represented the antithesis to Youngstown in mission, culture, outlook, and most extremely, in economic mobility. The opportunity gap created by a failure in adaptation was clear at the airport or at Thanksgiving. If we continue down the path of focusing only on the high-flying builders and engineers of artificial intelligence tools, we will produce one hundred Youngstowns for every one Silicon hub – a country that is not able to benefit or question the benefits of technology in the hands of a select few.
I am confident that if students were allowed to explore these tools, in synthesis with formal learning and with guidance from a qualified educator, they could explore, learn, and internalize a lot more than they are currently given the chance to do so.
