Interdisciplinary innovation is redesigning college math courses, with modernized quantitative reasoning options, both for students pursuing non–math-intensive majors and for those seeking to earn degrees in STEM fields. California is no exception, at least for students who begin their degree at a four-year university.
At UCLA, most life sciences majors meet their math requirement through a specialized course developed by life sciences faculty. At Berkeley, Foundations of Data Science—a cross-disciplinary offering created by faculty from departments including statistics and computer science—is one of the most common courses non-STEM majors take to meet their math requirement.
Developed during the last decade, these new offerings respond to leading mathematicians’ concern that traditional college math courses “leave students with the impression that all STEM fields are dull and unimaginative,” and a related call to develop courses that keep pace “with the large and rapid changes in how the mathematical sciences are used in science, engineering, medicine, finance, social science, and society at large.”
CSU students, too, are benefiting from innovations to make math learning more interesting and relevant: In recent years, San Diego State students have met their math requirement through courses such as Geographic Information Science and Spatial Reasoning (Geography), Infections and Epidemics (Public Health), and Introduction to Logic (Philosophy). Similarly, San Francisco State students have access to Quantitative Reasoning for Civic Engagement (History and Political Science), while Fresno State offers Statistical and Computer Applications in Criminal Justice for Criminology majors.
Evidence has tied such interdisciplinary approaches to improved success in college generally, and also in specific STEM fields. Their growth in California was made possible, in part, by a 2018 CSU policy that broadened the definition of general education math. Taking its lead from a faculty task force, that policy explicitly recognized the value of creating new quantitative reasoning courses that better align with students’ educational objectives. The task force explicitly noted that community college students should have access to the same opportunities available to university students.
However, due to complex transfer and articulation policies, community colleges have been slow to make such options available. And now, just as the promise of these courses is becoming evident, an imminent new policy runs the risk of curtailing them altogether, potentially preventing community college students from taking advantage of similar opportunities.
As part of a well-intended effort mandated by recent legislation (AB 928), faculty from California’s three higher education systems—the University of California, the California State University, and the California Community Colleges—have been asked to develop a common general education curriculum for the three systems. Though the goal is to facilitate more students transferring from community colleges to public universities, the composition of that curriculum could—intentionally or not—limit the ability of community colleges to offer the very type of quantitative reasoning courses that public universities have been expanding.
Currently, the CSU recognizes a broader range of general education quantitative reasoning courses than many UC campuses do. If the new curriculum defaults to UC’s more restrictive description of general education math or even strikes a balance between the two, community colleges may have to stop offering innovative math courses or cease adopting new ones, including courses that are offered currently to UC or CSU students.
Letting that happen would be a huge mistake. It would mean defaulting to outdated and myopic notions of mathematical rigor that have consistently been debunked. To argue that only traditional mathematics and statistics courses represent valid preparation in quantitative reasoning (as an earlier proposal from the systems’ faculty appeared to do) does not reflect the current role of math in society or students’ quantitative needs or experiences.
In developing UC Berkeley’s data science course, a 2015 faculty committee wrote that the course—which merges inferential thinking, computational thinking, and real-world relevance—“should not be viewed as ‘going soft on the math.’” They noted that “conceptual understanding can be developed, perhaps even better developed, through direct experience and computational actions performed with one’s own hands, rather than through symbolic manipulation.”
“Students today,” the authors said, “are more familiar with computational manipulations of representations of the real world than they are with symbolic idealizations of it.” Not only would Berkeley’s soon-to-be-launched course make it “possible to actually learn more in less time,” but, the committee also noted, “More of what is learned will be useful as well as more likely to ‘stick.’”
Learning more in less time and retaining it longer is a compelling argument for broadening undergraduate course offerings at UC Berkeley. Surely, the same argument applies to students throughout California and beyond. The laudable goal of streamlining transfer pathways should not be allowed to interfere inadvertently with making modernized quantitative reasoning courses available to undergraduates who begin at community colleges.
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