What are the course names and/or course numbers you are referring to?
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Looks like required according to the linked pages for LA, SD, and SF school districts. However, high school graduation typically requires only D grades in required courses (though there may be overall GPA requirements higher than 1.0), so getting students through algebra 2 at D grade achievement seems more possible than getting them through at a level that prepares them reasonably for more advanced math.
For comparison, the UC Berkeley precalculus course (MATH 32) filled last fall with almost 300 students. Berkeleytime link
right, but note PreCalc is not remediation as only math thru Alg II is a requirement for admission.
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Whether one relies on Data Science or Algebra II, these numbers don’t add up. Thousands of freshman aren’t failing out of remedial classes and UCLA every year. UCLA’s freshman retention rate is 97%, which is comparable to other elite schools.
I don’t think this is accurate either. Generally, CC transfer route doesn’t exist to replace kids who drop out. Rather, it increases the class size beginning junior year. This increase is expected and built into the system.
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What I am curious about is whether the data science proposal is in addition to what is currently available, or is a replacement to the current curriculum — ie replace algebra 2 with data science for “all” students. If it is the latter, then I feel strongly opposed, to the extent that I care about California at all. If it is just offered in addition, I feel that i don’t care.
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It is not. For HS, read Chapter 8 of the proposed framework.
Chapter 5 is devoted to just data science and provides recommendations on how it can be integrated into math education K-12.
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It appears to me that it could be a replacement for some students, if they so choose. Because it’s a path of least resistance, more students could be encouraged to take that path.
The question in my mind is how a student can become data literate without understanding the underlying math fundamentals. Would the student become pseudo data literate and prone to misinterpreting data, or worse, having a false confidence in unreliable data?
Take a glance at Chapter 5. It goes into examples of how data literacy should be incorporated into math starting at Kindergarten. It differentiates data literacy and data science.
The CHE article states that some quant professors are worried that the HS data science options are data literacy and not data science. They are worried that the STEM work force will shrink because students will choose data science rather than Calculus. Is the possibility of the STEM work force shrinking a real concern?
What Are Data Literacy and Data Science?
Many groups have used different terms to describe the ability to work with and derive meaning from data. These terms include statistical literacy, data literacy, data fluency, and data acumen. Because a full discussion of the academic and industry differences between these terms is outside the scope of this framework, the rest of this chapter uses the terms “data literacy” and “data science.”
Wolff and colleagues (2016, 10) describe “data literacy” as the ability to ask and answer real-world questions from large and small data sets through an inquiry process, with consideration of ethical use of data. It is based on core practical and creative skills, with the ability to extend knowledge of specialist data handling skills according to goals. These include the abilities to select, clean, analyze, visualize, critique, and interpret data, as well as to communicate stories from data and use data as part of a design process.
Within most mathematics courses, students at the prekindergarten through grade twelve level will be building strong mathematical foundations and be engaged in work that supports data literacy; all California students should graduate from high school with data literacy. However, students should also have access to experiences that extend beyond what many currently experience in their mathematics classrooms and that prepare them for future work in an emerging field called data science.
Data science is an emerging discipline, and there is yet to be a consensus on exactly what content constitutes “data science.” Data science education organizations have conceptualized it as a cyclical process that includes problem formulation, data collection, data analysis, and interpretation and communication of findings (GAISE II, IDSSP, 2019). While the data science field continues to be shaped by emerging technologies and techniques deeply influenced by academia and business sectors, most definitions of data science recognize an intersection of mathematics, statistics, and computer science. Data science may also include domain knowledge, ethics, and communication skills as well as specific approaches such as data mining (especially for data collected through the internet and electronic devices) and machine learning. Although students may not encounter topics such as machine learning until after high school, kindergarten through grade twelve (K–12) mathematics provides an essential foundation in statistical concepts necessary for future learning in data science.
Data literacy and data science should be thought of more as a continuum than as distinct concepts. The focus of data literacy is the ability to use an inquiry process to extract answers to real-world questions from data sets. All K–12 graduates should have developed data literacy through rich data experiences in each grade level. Data literacy is part of data science, but data science also includes advanced mathematics, statistics, and computational skills that build upon—and go far beyond—the content contained in the K–12 California mathematics standards.
Not sure what state you are in. Proposed CDE framework points out:
California is not alone in giving attention to this growing field. State departments of education in Georgia, Ohio, Oregon, Utah, and Virginia are also exploring how to increase access to data science concepts for their students, whether by revising mathematics standards, creating new mathematics course pathways or frameworks, or providing micro-credentials in data science for teachers (National Center for Education Research, Institute of Education Sciences, 2021).
Aren’t those all sorts of applications of things? I mean if you have a solid foundation in mathematics, data science wouldn’t be a difficult field to enter.
Why not just do basics well in K-12 - math, reading, writing. Let colleges take it further be it data science or calculus.
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I don’t know the justification, other than what is written in the framework. My impression, from what I read, is that they are trying to incorporate project based learning into K-12 math, similar to what they did with NGSS.
UC has reversed its decision to allow data science as a substitute for Algebra 2:
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it said, while the working group is studying the issue, nothing would differ for UC admission requirements, and there would be no impact on the upcoming class of applicants. “Thus, to reiterate, no changes were made to the mathematics admissions requirement, or to what the University deems to meet that requirement for undergraduate admission to the system at this time,”
It is interesting, as this article seems to be the result of a couple of leaked emails. The pdf of the language of what the committee wants changed can be read here.
The UC Office of the President HS Articulation team has approved hundreds of data science courses in CA - 43 of which are currently weighted. The committee’s concern seems to be with stand alone data science as a math course. However, many of the courses that the UC Articulation team approved, are in the Science (D) section of A-G requirements. Some approved courses look like they are a CS/Data Science or Stats/Data Science combo class. The CS/Data Science courses seem to be in D and Stats/Data Science courses in C.
No, the committee’s concern was replacing the minimum requirement (Algebra II) for admission to the University of California (and CSU) with any other math course, such as Data Science or Stats…
Yes, Data science is an approved math elective, just as is AP Stats, and perfectly acceptable for a 4th year math course or even 5th math course (some kids double up math). But AP Stats does not replace Alg II to meet the minimum math requirement for admission.
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That’s how our kids’ HS does it. All students take integrated math 1-3 which gets them to precalculus in 11th grade (advanced math 1-3 is an accelerated track where they start calculus in spring semester of 11th grade). In 12th grade they have options including calculus, stats, data science, etc.
Got it.
I am a little confused over requirements. So if you are entering a UC system as a freshman, you need to first complete algebra 2? But then why do they accept transfer students who have only taken stats? At our community college all you need is a basic statistics class to graduate with a two year degree and transfer. Why are entry requirements so different?
yes, applying directly from high school requires the successful completion of high school Algebra II. (That has been a UC requirement for quite a few years.)
Applying as a transfer requires the successful completion of at least one college course “in mathematical concepts and quantitative reasoning.” But note, successful transfers also need to meet transfer requirements for the Major, so if one is applying for a Major that requires Calculus II, for example, that must also be completed prior to transfer.
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So if you are applying for history out of high school, you need algebra 2. If you are applying for history out of community college, you get away with basic stats. I wonder if it’s because transfers can’t change majors? Just thinking out loud because otherwise it makes no sense.