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December 6, 2021

Data Science Collides with Traditional Math in the Golden State

(Who is Danny/Shutterstock)

Is traditional math still important in data science? Or can a new curriculum based on data science replace some of traditional mathematics courses while promoting greater racial equity? These questions are at the heart of a debate that’s heating up in academic circles this month.

The issue of racial disparities in middle school and high school math classes has been a subject of concern for some time. In San Francisco, public school educators have pulled back on the availability of advanced math classes in an attempt to close the performance gap.

San Francisco’s approach is the model for a new math framework proposed by the California Department of Education that has been adopted for K-12 education statewide. Like the San Francisco model, the state framework seeks to alter the traditional pathway that has guided college-bound students for generations, including by encouraging middle schools to drop Algebra 1 (the decision to implement the recommendations is made by individual school districts).

This new framework has been received with some controversy. Yesterday, a group of university professors wrote an open letter on K-12 mathematics, which specifically cites the new California Mathematics Framework.

“We fully agree that mathematics education ‘should not be a gatekeeper but a launchpad,’” the professors write. “However, we are deeply concerned about the unintended consequences of recent well-intentioned approaches to reform mathematics, particularly the California Mathematics Framework.”

Frameworks like the CMF aim to “reduce achievement gaps by limiting the availability of advanced mathematical courses to middle schoolers and beginning high schoolers,” the professors continued. “While such reforms superficially seem ‘successful’ at reducing disparities at the high school level, they are merely kicking the can to college.”

Data science also plays a role in the debate, since the California Math Framework also brings recommendations centered around the use of data science. In fact, it devotes an entire chapter to the data science, which it defines as “the process of uncovering the stories hidden within data.” The framework encourages teachers to use data science techniques to present lessons, as well as teaching some of the basics of data science as well.

A visualization of Kira’s dog’s interactions (California Mathematics Framework)

The CMF also provides examples of how data science and statistics power the investigation process, and describes resources that will be available to students, such as the Common Online Data Analysis Platform (CODAP), which the CMF describes as “a set of databases that will be interesting to school students, such as data on earthquakes, mammals, stars and cities….”

Considering the current shortage of professional data scientists, which are projected to get even worse in the coming years, the decision to prominently position data science in the new math framework could be seen as forward-thinking. To be sure, all sorts of data analysis techniques will be important job skills to have in the future, and getting students familiar with the concepts and terminology can position them for greater success in the future.

However, the university professors take issue with the Golden State’s treatment of data science as a whole, specifically the possibility that the proposed data science education would replace the more traditional approaches to teaching mathematics.

“Another deeply worrisome trend is devaluing essential mathematical tools such as calculus and algebra in favor of seemingly more modern ‘data science,’” the professors write in the Open Letter. “As STEM professionals and educators we should be sympathetic to this approach, and yet, we reject it wholeheartedly.”

Specifically, the professors reject the CMF’s attempt to position data science as a more equitable alternative to traditional math. They take issue with CMF statements on the matter, such as:

“The data science field provides opportunities for equitable practice, with multiple opportunities for students to pursue answers to wonderings and to accept the reality that all students can excel in data science fields.”

The CMF also states that “non-traditional pathways” (such as the proposed data science-based approach “[focus] on the use of inclusive teaching practices … allow more equitable access to authentic mathematics for all students, and necessitate a view that mathematics is a beautiful and connected subject, both internally and to the greater world around.”

In a fuller analysis that accompanied the Open Letter, the principle authors took strong issue with those statements. “To put it mildly, there is no basis for the assertion that ‘data science’ is inherently more equitable than algebra or calculus, and many documented uses of  ‘data science’ amplify inequity.”

In an accompanying post on the blog run by University of Texas at Austin Computer Science Professors Scott Aaronson, Boaz Barak of Harvard and Edith Cohen of Google, two of the four principle authors of the Open Letter, wrote:

“The CMF also promotes trendy and shallow courses (such as a nearly math-free version of  ‘data science’) as recommended alternatives to foundational mathematical courses such as Algebra and Calculus. These courses do not prepare students even for careers in data science itself!”

So far, the Open Letter has been signed by nearly 750 college professors, including winners of prominent awards such as Fields Medal, the Turing Award, and the Nobel Prize. It includes dozens of members of the National Academy of Sciences; and faculty from many of the top univerities, including Stanford, Berkeley, MIT, Harvard, and more. You can read the letter here.

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