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I’ve been reporting on data science education for two years now, and it’s become clear to me that what’s missing is a national framework for teaching data skills and literacy, similar to the Common Core standards for math or the Next Generation Science Standards.
Data literacy is increasingly critical for many jobs in science, technology and beyond, and so far schools in 28 states offer some sort of data science course. But those classes vary widely in content and approach, in part because there’s little agreement around what exactly data science education should look like.
Last week, there was finally some movement on this front — a group of K-12 educators, students, higher ed officials and industry leaders presented initial findings on what they believe students should know about data by the time they graduate from high school.
Data Science 4 Everyone, an initiative based at the University of Chicago, assembled 11 focus groups that met over five months to debate what foundational knowledge on data and artificial intelligence students should acquire not only in dedicated data science classes but also in math, English, science and other subjects.
Among the groups’ proposals for what every graduating high schooler should be able to do:
- Collect, process and “clean” data
- Analyze and interpret data, and be able to create visualizations with that data
- Identify biases in data, and think critically about how the data was generated and how it could be used responsibly
On August 15, Data Science 4 Everyone plans to release a draft of its initial recommendations, and will be asking educators, parents and others across the country to vote on those ideas and give other feedback.
Here are a few key stories to bring you up to speed:
Data science under fire: What math do high schoolers really need?
Earlier this year, I reported on how a California school district created a data science course in 2020, to offer an alternative math course to students who might struggle in traditional junior and senior math courses such as Algebra II, Pre-Calculus and Calculus, or didn’t plan to pursue science or math fields or attend a four-year college. California has been at the center of the debate on how much math, and what math, students need to know before high school graduation.
Eliminating advanced math ‘tracks’ often prompts outrage. Some districts buck the trend
Hechinger contributor Steven Yoder wrote about how districts that try to ‘detrack’ — or stop sorting students by perceived ability — often face parental pushback. But he identified a handful of districts that have forged ahead successfully with detracking.
PROOF POINTS: Stanford’s Jo Boaler talks about her new book ‘MATH-ish’ and takes on her critics
My colleague Jill Barshay spoke with Boaler, the controversial Stanford math education professor who has advocated for data science education, detracking and other strategies to change how math is taught. Jill writes that the academic fight over Boaler’s findings reflects wider weaknesses in education research.
What’s next: This summer and fall I’m reporting on other math topics, including a program to get more Black and Hispanic students into and through Calculus, and efforts by some states to revise algebra instruction. I’d love to hear your thoughts on these topics and other math ideas you think we should be writing about.
More on the Future of Learning
“How did students pitch themselves to colleges after last year’s affirmative action ruling?,” The Hechinger Report
“PROOF POINTS: This is your brain. This is your brain on screens,” The Hechinger Report
“Budget would require districts to post plans to educate kids in emergencies,” EdSource
“Oklahoma education head discusses why he’s mandating public schools teach the Bible,” PBS
This story about data science standards was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education.
“[Data science] classes vary widely in content and approach, in part because there’s little agreement around what exactly data science education should look like.” Research has found considerable variability in mathematics classes, despite the existence of standards. It may be that there’s LESS variability in data science classes because there aren’t as many choices for course materials and professional development.
The Hechinger Report has noted these sources of variability in mathematics classes:
Anxious teachers may spend less time on math: “Math specialists say [math anxiety] is a pervasive issue in elementary classrooms, where educators are typically expected to teach every subject, and it often leads to teachers spending less classroom time on math content.” https://hechingerreport.org/teachers-conquering-their-math-anxiety/
DIY instructional materials: “teacher-made materials may sacrifice the thoughtful sequencing of topics planned by curriculum designers.” https://hechingerreport.org/proof-points-many-high-school-math-teachers-cobble-together-their-own-instructional-materials-from-the-internet-and-elsewhere-a-survey-finds/
I’ve discussed other reasons for variability here: https://mathvoices.ams.org/teachingandlearning/is-data-science-the-new-discrete-mathematics/.