The Bootstrap Blog

Data Science is Here

Oh great, now we have to teach another new class? Exhausted teachers everywhere

As we predicted in that blog post half a decade ago, Data Science has become the Next Big Push. We've been running Data Science professional development and serving students nationwide since 2017. In 2019, we organized the first meeting track at CSforAll focused on Data Science, in an effort to build consensus around what Data Science could look like in K-12.

Unfortunately, another prediction from 2017 has also come true: Standalone Data Science is now competing for precious classroom time with other standalone classes.

To be clear, we're all for adding new electives! We support any implementation of standalone Data Science classes as a "both/and" proposal, rather than an "either/or" one. Unfortunately, schools have limited room in the budget and time in the schedule - which means Standalone Data Science must replace a class. This is especially true in the most under-resourced schools and communities, who must always make difficult choices.

So regardless of how Standalone Data Science is characterized, the reality is that it must be implemented as a replacement for an existing course. Nowhere is this clash more evident than in Algebra 2, especially in California. Is tossing Algebra 2 the only way to roll out Data Science? We don't think so. In fact, we don't even think it's the best way!

Some are quick to criticize Data Science for lacking rigor, or label it "watered down mathematics". As curriculum developers, we reject the notion that an entire subject can be evaluated for rigor at all! A particular curriculum might have or lack rigor, but not an entire discipline.

For students who want to major in STEM fields, the content covered in Algebra 2 is absolutely essential! Here are just a few examples:

  • Quadratic relationships are required to understand things like acceleration in Physics
  • Exponential relationships show up all the time in Biology and Chemistry
  • Logarithms are essential for measuring the strength of earthquakes in Geology or the brightness of stars in Astronomy.
  • Inverse functions are necessary for understanding things like compression and encryption, which are important topics in computer science, and cybersecurity.

Students who start college without these topics wind up behind their peers before they've even started, in these fields and many more.

Our concern is that Standalone Data Science is pitched as a replacement for a course that it doesn't resemble at all. AP French and AP Spanish are both rigorous classes in foreign languages, and those languages share a ton of vocabulary and grammar. These classes are arguably much closer in content to one another than Data Science and Algebra 2! Yet a student who's taken two years of French would be up in arms to discover that the AP they expected to take has been substituted for with AP Spanish.

It was entirely predictable that pitting Data Science against Algebra 2 would result in this exact backlash. In a nation as polarized as ours right now, we are saddened to see Data Science politicized the way it has been - but it didn't have to be this way.

Frankly, we are surprised that people aren't talking about an integrated approach. Data science, in particular, integrates into lots of disciplines: not only statistics or math but even social studies. Plus, it means students can be exposed to it several times, over the years, in many contexts, gradually building up knowledge and seeing it applied broadly. It also means schools don't have to find money to hire all these new teachers, make space in the curriculum for them to teach, and deal with what they may be displacing.

Multiple states (including Illinois, New York, Oklahoma, and others) have or are about to adopt legislation that requires integrating computing into all elementary and middle schools, thereby acknowledging the practical value of such an approach. It seems inevitable that data science will (and should) also go this path.

Of course, integration requires careful design: e.g., you can't teach a social studies class like a stats class. Mastering the details of integration is of course exactly what we at Bootstrap have been working on for over a decade now. (We are actually already partnering with several of the above states on computing.) Specifically, our data science materials have been tested for half a decade, are already integrated into several math, science, history, and social studies classes in grades 5-12, and are available for free to anyone who wants them.

We think Data Science doesn't have to be Algebra 2's biggest enemy. With a little careful thinking, in fact, Integrated Data Science can be Algebra 2's biggest cheerleader!

Our DS4Everyone commitment this year is all about making this vision a reality for Algebra 2. Integration isn't a free lunch - the work and forethought required is enormous. But we've been thinking deeply about these issues broadly for the last fifteen years, and about Data Science in particular for the last five. We think every child deserves access to the content of Algebra 2, and has the right to a genuine introduction in Data Science. We hope others will look beyond the false dichotomy, and join us in developing meaningful, rigorous integrated materials that respect the ways Algebra 2 and Data Science can work together.

Posted June 22nd, 2022