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Last year, I began reporting on the growing interest in teaching young people about data science amid calls that Algebra II and other higher-level math classes are being taught in outdated ways and need to be modernized. Experts were already raising concerns about falling math scores before the pandemic, and those scores nationwide have only continued to worsen.
There’s no easy answer – math experts, STEM professors, high school educators, parents, advocates and even students have vastly different opinions on what math knowledge and courses should be required for students to succeed in college and careers.
Nowhere has this been clearer than in California. As I wrote in my latest story, co-published with The Washington Post, the state’s public higher education system has gone back and forth on whether data science (an interdisciplinary field that combines computer programming, math and statistics) and other statistics-based courses fit into existing math pathways and can serve as an alternative to Algebra II in admissions.
But missing from these debates was the voices of students and educators – those most affected by any decisions made by the state’s public university system. I wanted to see for myself what students were learning in high school data science classes, why they were signing up for the course and how decisions about which math classes to take were being determined.
In December, I visited Oxnard Union High School District, which launched a data science pathway in 2020. The class targeted students who didn’t plan to major in STEM fields in college, as well as those who planned to attend a community college or go straight into the workforce or military. A “math class for poets” was how the district’s superintendent, Tom McCoy, had jokingly described it.
From my visits to the district’s high school data science classes and my conversations with teachers and students, two things became clear: The course’s structure is very different than a traditional math class – it’s an applied, project-based learning course in which students collaborate closely as they learn the material. And the way different teachers and schools approach the class differs greatly, even within a single district. Some teachers emphasize data literacy (teaching students how to read and analyze data); others incorporate math concepts from algebra and statistics; and still others may inject more computer programming or coding.
That variation — both in how the classes are taught and their content – has added to concerns that data science courses are low quality and insufficiently rigorous. And it’s in part why there’s an emerging push to develop standards around the course, and tackle the question of what an effective data science course should look like.
Much of the concern around data science in California centers around three programs — Introduction to Data Science, Youcubed and CourseKata — that make up the majority of data science courses available there. According to a recent report from University of California committee that sets admissions standards, none of the courses “even come close to meeting the required standard to be a ‘more advanced’ course,” and are more similar to data literacy courses than advanced mathematics. (Oxnard Union uses a different curricula, one developed by ed tech vendor Bootstrap.)
Mahmoud Harding is the instructional design director at Data Science 4 Everyone, a national initiative based at the University of Chicago. He co-developed a high school data science program at the North Carolina School of Science and Mathematics and teaches a course at North Carolina State’s Data Science Academy. He said a high school data science course should help students find more real-world applications for concepts they learn in algebra.
In addition, the class should build conceptual knowledge of statistical topics through computation, visualizations and simulations, and help students understand bias within data and ethical concerns in using flawed data. Data science courses also need to be substantively different from statistics or computer programing courses, he said, noting that data science is “inherently interdisciplinary.”
“I don’t think a data science course is the same as an Algebra II course,” Harding said. “But it doesn’t mean that a data science course isn’t rigorous, or it doesn’t mean that you can’t matriculate into higher forms of algebra because you’ve taken data science.”
Harding’s group, Data Science 4 Everyone, is helping to lead the new effort to develop standards for data science. Zarek Drozda, the group’s executive director, said this year it will convene a working group of experts, K-12 educators, STEM professors, curriculum providers, state and district leaders, students and industry and workforce professionals including those with tech companies, to help create a list of recommendations of baseline data science standards.
As career opportunities involving AI, computing and data increase, Drozda said it is “critical” that we think about the foundational knowledge students need by the time they graduate from college. The group is engaging people from all sides of the data science debate to look critically at the courses currently offered and identify how to create classes that will better meet the needs of students.
Drozda said he also hopes the working group will consider how exposure to data science classes can help more students get excited about STEM fields that don’t necessarily require a four-year degree.
“I think there’s a false perception that we are trying to replace fundamental mathematics,” Drozda said. “In reality, we are trying to modernize, add options and enhance the relevance of mathematics and prove to students that math matters in the 21st century.”
This story about data science classes was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the Hechinger newsletter.
I don’t understand the push to teach Data Science rather than Statistics. What’s the difference? I suspect that DS has less math and is therefore easier, but perhaps I’m too cynical.