“A century ago it was Greek and Latin. Today, it’s the quadratic formula.” That’s what Opportunity Institute founder Christopher Edley, Jr. told the PBS NewsHour about how mathematics requirements can serve as arbitrary educational filters that disproportionately hurt kids who have less access to advanced instruction.
The analogy is apt. Some notoriously tricky Algebra 2 topics are not only hard to master, like classical languages. Their weight in the curriculum is also increasingly hard to justify, especially when there are relevant and rigorous alternatives, like data science.
The nature of math is changing dramatically, notes Stanford mathematician Keith Devlin — and so should math instruction. “What caused many people problems over the centuries, was that before we had technologies that could handle the formal symbol-manipulation stuff, the only way to employ our innate capacity for mathematical thinking was to train the brain to do those manipulations,” wrote Devlin in a 2018 blog. “Manipulating algebraic symbols with logical precision is most definitely not something our brains evolved to do.”
There’s little need to school ourselves to do what machines can now handle. This frees up our minds to use math to solve real-world problems. Devlin calls this a revolution: “It has brought ‘using math’ into the mainstream of human group activities we naturally find enjoyable. At heart, mathematical thinking is little more than formalized common sense. It always has been. Which means it is something we can all do.”
It just takes practice, a democratic reality to which math education needs to catch up. Re-thinking classes such as Algebra 2 is part of that. The National Council of Teachers of Mathematics has called for eliminating some “obsolete legacy content” like “traditional symbolic manipulation” from high school math in order to make room for more relevant topics.
Data science belongs high on that list of relevant subjects, as schools and universities are beginning to realize. “Ever-growing demands on our capacity to reason reliably, intelligently, and creatively from data will change how our students tune their academic trajectories, carry out their careers, and live in their world,” wrote a University of California at Berkeley faculty task force in 2015. They were arguing for making data science an integral part of liberal arts education as well as designing a general education math course based on data science.
One challenge in promoting such changes is the amorphous meaning attached to “data science.” It is an interdisciplinary field combining computing, statistics, and information management, often in the context of real-life data used in various disciplines. The Berkeley faculty committee also called it a “reinvention of statistical education.”
A related challenge is the very interdisciplinary nature of data science. The field has evolved across many disciplines, as evidenced by the composition of the 2015 Berkeley task force. It included faculty from history and political science in addition to engineering, statistics, and physics. Unlike those fields, data science doesn’t have its own power centers in university academic departments, disciplinary societies, and well-established journals.
But as undergraduates vote with their feet, such obstacles are being surmounted and data science is developing a foothold. UC Berkeley’s task force was launched partly in response to a quintupling in the number of students majoring in statistics or computer science over five years. Over the same period, the number of students from outside those majors taking statistics and computer science courses also grew exponentially, such that more than half of Berkeley undergraduates were taking one or both.
Berkeley’ Foundations of Data Science course became the fastest-growing course in campus history after it was launched four years ago. In addition, the campus offers various discipline-relevant data science courses including:
- Data Science and the Mind
- Children in the Developing World
- Race, Policing, and Data Science
- Moral Questions of Data Science
As of last year, the campus also had launched a new major in Data Science. Research universities around the country are following suit. Early this year, for example, the University of Virginia announced plans to start a School of Data Science with a $120 million donation, the largest in the institution’s 200-year history.
Such developments help pave the way for recognizing the importance of teaching data science before college. So do the Common Core State Standards, which added an emphasis on statistics and probability to the list of math topics students should learn in high school.
A promising high school course called Introduction to Data Science (IDS) got its start in Los Angeles. With a grant from the National Science Foundation, UCLA faculty worked with LA Unified to develop IDS. Now five years old, the class incorporates key Algebra 2 topics. But it emphasizes basic probability and statistics along with computer programming. Students analyze some public data sets, but they also collect and probe data on their own stress levels and snacking habits. They learn to create and interpret statistical plots and develop linear regression models.
Like Berkeley’s course, IDS has grown quickly since its initial pilot. It is now being offered in 40 schools across 17 districts, and demand is growing for further replication. The main barrier to expansion is “un-training” our brains from old ideas about math learning:
In light of the growth in data science and the need for data-savvy professionals in a range of fields, it’s only a matter of time before these old approaches get sidelined. But there is no reason to wait. Given the advantages to students of broadening math curriculum to include fields like data science, education leaders can seize the opportunity now. They should update their policies and discard arbitrary requirements just as their predecessors said goodbye to Latin and Greek.
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