July 2024

To the University of California Board of Regents: Clarifying The Role of Data Science Courses

Letter from Pamela Burdman
To the University of California Board of Regents: Clarifying The Role of Data Science Courses

Just Equations founder and executive director Pamela Burdman has written a letter to the University of California Board of Regents, calling on the university to clarify and consider how data science and statistics courses will be used and evaluated as part of an admissions process that still leans heavily on Algebra II content.

Dear Regents Reilly and Leib,

I write as the founder and executive director of Just Equations about the next steps in implementation of the Area C math requirement for admissions eligibility. Our California-based policy institute is focused on the role of math in education equity.

We appreciate that in its latest report for the Board of Admissions and Relations With Schools (BOARS), the Area C Workgroup lays out its expectation for high school senior-year math courses that qualify for Area C in more depth than in its prior report. This is a first step toward clarifying existing confusion about the status of courses that have been accepted as third-year or fourth-year courses.

The workgroup’s determination that, in order to validate Algebra II, the third course in the high school math sequence must cover or rely upon the vast majority of Algebra II content clarifies its expectation that all students receive grounding in two years of algebra. Given that priority, it makes sense that neither data science or statistics courses meet the requirement to be a Category 2 course. The workgroup’s recognition of a potential role for data science courses in the college-preparatory math curriculum is also encouraging. However, other decisions of the committee remain confusing, and we urge Undergraduate Admissions to promptly clarify how those decisions will be interpreted and implemented. In particular, the workgroup divided courses that don’t qualify for Category 2 into recommended (Category 3) and non-recommended (Category 4) courses. The distinctions among the categories are, at best, confusing.

As such, the criteria for these courses are not terribly transparent. How will the emphasis on “abstract  mathematical concepts,” “a workout in Algebra II,” and making math visible (rather than hiding it behind  computational black boxes) be used in evaluating future courses? And what is the role of the examples  of data science topics in Appendix B and how do they relate to the prerequisites for statistics courses in Appendix A, given that previous data science courses were approved as statistics courses? 

It is important that in implementing these new categories, UC promptly clarify for schools, counselors, students, and families what these distinctions mean. For example, 

  • How will students know whether a course other than Precalculus is or is not a recommended course? 
  • Only three data science courses were analyzed. What about other existing data science  courses? What is their status? 
  • What about previously-approved courses in areas such computer science and discrete math? Are those recommended courses? 
  • Will a student’s choice of a Category 3 vs. a Category 4 course influence admissions decisions? If so, how? While it’s been stated that these choices do not affect eligibility, how will they affect  competitive eligibility? 
  • Some of the high school data science courses that BOARS says will be in Category 4 are comparable to college statistics and data science courses that meet general education requirements at many UC and CSU campuses. Are the high school courses being held to a different standard than college general education courses? If so, why? 
  • The Area C designation applies to both UC and CSU, which means that a Category 3 statistics course “builds on” the content of Algebra II. But CSU’s Quantitative Reasoning Task Force has stated that success in college statistics requires proficiency “in most of the K-8 curriculum as well as several topics from Algebra I.” Why does BOARS seemingly expect high school statistics courses to build on Algebra II when college statistics courses do not? 
  • Since one of the three high school data science courses, CourseKata, is offered at community colleges (as is UC Berkeley’s Data 8 course), can a student get college credit and Area C credit for this course if taken via dual enrollment, even though the committee states it is not a recommended Area C course at the high school level? 
  • How will course reviews recognize the ways in which some statistics courses might be “significantly more conceptually challenging” than algebra courses even though they are less “algebraically intensive,” to quote the CSU Quantitative Reasoning Task Force? Why does the UC workgroup not recognize the possibility of academic challenge outside the algebraic realm?

Course developers also need to understand the criteria for these distinctions as they design or update their courses. Developers of courses deemed “non-recommended” are entitled to know what materials were reviewed, and to provide any missing information for future review, since none were apparently given the opportunity to share their course materials with the workgroup.  

Lastly, we agree with BOARS about the need for further research on improving students’ math  preparation for college, but we question the assumption that this need centers only on calculus. Per the report’s own data, if undeclared students are included, STEM majors—those with the greatest need for calculus preparation—remain a minority of UC students. The statistical and data sciences are increasingly important to students across the university, whether in STEM, social science, humanities, or arts. 

While improving calculus preparation and success is undoubtedly an important goal (and a topic on which Just Equations has written) research is also sorely needed into how best to provide students with the data skills and statistical fluency they will need in the 21st century—and also into the relevance of these fields for STEM research. The report makes the assumption, with little evidence, that algebraic intensity is necessary for success in statistics and data science, but does not consider that the  reverse—statistical and data competency strengthening students’ interest and performance in algebraic-intensive pursuits—could be true. 

Will the Academic Senate and Office of the President work together to advance this broader research goal? On behalf of Just Equations, I sincerely hope so. Please do reach out if we can be helpful in this endeavor.

Sincerely,

Pamela Burdman 

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