(This story is adapted from an article that originally appeared in EdSource, January 25, 2023, by Ji Y. Son, professor of psychology at California State University, Los Angeles and James W. Stigler, distinguished professor of psychology at UCLA.)
Choosing the right math courses in high school is a question that families have long wrestled with. That’s even truer in recent years, now that a strong math background is seen as a prerequisite for higher-paying careers in science, technology, engineering, and mathematics (STEM) fields.
The traditional sequence is algebra I and II, geometry, trigonometry, and calculus. But the failure rates for algebra are extremely high in the U.S. So some schools have begun offering an alternative path through math courses that includes learning statistics and data science.
However, we think that students should not be forced to choose between the data science path, which can seem more engaging, relevant, and/or modern, and the traditional path through calculus, which gives students the opportunity to study the math they will need if they wish to pursue STEM-related careers. Why? All students could benefit from learning about statistics, data science, and coding. But if they plan to work in data science, or in almost any STEM-related field, they will need a deep understanding of algebra.
A strong high school math curriculum should encompass both algebra and data science. We think that many students would be better off, learn more, and have a greater interest in math if that were the case.
Algebra and data science each have their strength
Although some students thrive on the pathway to calculus, most do not. Algebra I is the single most failed course in U.S. high schools. Thirty-three percent of students in California, for example, took Algebra I at least twice during their high school careers. Students of color and from low-income families are overrepresented in this group.
Some argue that algebra as part of the pathway to calculus is less relevant in today’s world and that students would be better served by taking fewer courses in algebra and more in fields such as statistics and data science. The University of California, for example, has ruled that statistics and data science courses can be taken in place of Algebra 2 to meet its admission requirements.
Others push back against this approach, arguing that high-level participation in STEM careers will ultimately require calculus, and that luring students away from Algebra 2 and into data science will cut them off from these career opportunities – including jobs in data science!
Furthermore, many worry that students from disadvantaged backgrounds, who are at greater risk of failing Algebra I, will be those most likely to be tracked into these alternative math pathways, and thus more likely to be lost from the STEM-career pipeline.
Algebra without meaning can bore students
Arguments about what content should be included in high school mathematics fail to acknowledge the elephant in the room: We haven’t yet figured out how to teach the concepts of algebra well to most students.
Many students who pass Algebra 1 do not master the content in sufficient depth to prepare them for Algebra 2, much less higher-level STEM courses. And many students who do well enough in algebra find it boring and not relevant to their own lives.
Students cannot be faulted for their lack of interest in learning about the “steps” required to solve for X or dumb acronyms like FOIL (a mnemonic to help students remember how to factor polynomials). This view of what algebra is cannot sustain most students’ motivation to pursue STEM-related careers.
Algebra + data science = relevant and interesting
Data science is not an alternative to algebra. In fact, it very well may be the key to figuring out how to teach algebra well.
High school algebra, in our view, desperately needs data science, a catch-all term for the quantitative reasoning and mathematical ideas that go into working with data collected in the real world. Data science has the potential to make algebra relevant and interesting for students who want to understand and improve the world. Data science may be the best answer we have to the question most often asked by high school algebra students: How will I ever use this?
But just as algebra needs data science, data science needs algebra. The basic functions taught in high school algebra (e.g., linear, polynomial, etc.) are used to model patterns in data. We have been pursuing this possibility by developing a statistics and data science curriculum for high school and early college that emphasizes concepts core to both algebra and data science: functions and modeling.
Our students don’t learn about functions as mathematical abstractions, but use them as imperfect models to help understand and predict variation in the world. And they learn that even imperfect models are better than no model at all. Actual data, after all, is messier than abstract theory.
Happily, we also see that after students learn to construct simple models, they’ll often ask about how to model more complex patterns in data. Teachers are thrilled that students want to learn about exponential, logarithmic, and polynomial functions, which many students were exposed to without realizing their value. Students become hungry to learn some algebra!
Imagine a world where students feel a need for algebraic functions rather than feeling forced to learn them by the so-called “math people.” Imagine a generation of students who think an exponential function might be helpful for them to learn. If algebra can embrace data science and data science can do the same for algebra, we can all look forward to a world where students feel dissatisfied with their current knowledge and want to learn more math.
