Implementation

> Models for schools, districts, and states

There are many curriculum models through which individual schools, school districts, or entire states can integrate data science into K-12 education. Many stakeholders have already developed curricula and lesson plans for K-12 data science, or are actively working to do so.

 

Explore four possible models below, both for the mathematics curriculum, and across other subjects. Many have already been adopted by schools and districts, and some will be soon by entire states. Models like these can also complement and strengthen K-12 computer science education, and help bring CS into other subjects through modules or individual activities through simple, introductory coding.

1-Year Elective
Merge & Purge
Pathways
CBM

Less Modification

More Modification

 
 

1-Year Elective Course

>   Model: a one-year elective dedicated to teaching the basics of data science in late high school.

>   Example Program: UCLA CenterX Introduction to Data Science (IDS)

>   Implementation? Los Angeles Unified School District (LAUSD) adopted the course in 2014. For the 2020 school year, 3,200 students across 51 schools in Southern California are currently enrolled. The course is also approved as a "C" course in the University of California A-G requirements, and validates the Algebra II requirement. Two other courses—one in pilot phase and another in development for the 2021-2022 school year, are available below.

Available Curricula:

Questions?

We'd be happy to help you identify the right program or model for your school / district.

 

Merge & Purge

>   Model: Geometry & Algebra II are cut to ½-years, and ½-years of Data Science & Statistics are integrated.

>   Partner: Center for RISC / University of Chicago

>   Implementation? The Oregon Mathematics Project is proposing a similar 2+1 Curriculum Model.

Program Description:

This model proposes a "merge and purge" of traditional K-12 mathematics, typically organized into the Algebra I—Geometry—Algebra II sequence. By removing less relevant content, typical topics in Geometry and Algebra II are each condensed to a 1/2-year. The new space would then be filled by integrating a full year of topics in data science, statistics, and mathematical modeling using contemporary computational tools.  This teaches some of the same mathematical logic necessary for critical thinking skills, with more relevant, twenty-first century content. Better yet, practical skills for modern careers and daily life are taught more directly. The path to more advanced mathematics, such as Calculus, is also preserved.

 

Pathways

>   Model: A data science and statistics sequence becomes one available pathway during high school.

>   Partner: Dana Center Mathematics Pathways / U.T. Austin Charles A. Dana Center

>   Implementation? Pilots are being developed in partner districts in Georgia, Texas, and Washington.

Program Description:

"Mathematics pathways are part of a growing national movement designed to change the way mathematics is taught in higher education. In the “pathways model,” students are placed into course sequences designed to align with their personal interests, chosen fields of study, and career goals. Historically, college–level study has required that students, regardless of their academic or career goals, adhere to standardized mathematics course sequences that may not be relevant to their needs. In fact, traditional mathematics courses have been found to be the most significant barrier to degree completion for all fields of study."

 

CBM: Computer-Based Mathematics

>   Model: A problem-centered, complete redesign of math curriculum that assumes computers exist.

>   Partner: ComputerBasedMath.org / Wolfram

>   Implementation? Ongoing pilot in Estonia (surprised? Read about Estonia's success in education).

Program Description:

"The CBM curriculum is unique in assuming computers by default, and so avoiding the need to learn most of the complex hand–calculation skills that were vital to our predecessors. Written from core guiding principles that firmly focus on the needs of learners for jobs and everyday life in the near future, the new curriculum is problem–centred versus the traditional mechanics–centred curriculum. Students are taught to solve problems using the tools available to them, rather than learning isolated, out–of–context skills, like completing a long division problem or calculating standard deviation."

Want to bring one of these models to your school, district, or state?

Superintendents, legislators, board members, or other policymakers, please:

Ready to get serious? Pilot a program:

Open Pilots

> Programs looking for schools to pilot content

CourseKata Statistics and Data Science

CourseKata is an interactive online textbook that provides a rigorous introduction to modern data science and statistics. Concepts are taught in the context of data analysis, which students learn to do using R. Students go back and forth between doing and understanding, learning the practice of data analysis while, at the same time, learning to think using statistical ideas. Every bit is taught in relation to the whole, resulting in a more coherent understanding of the domain. Can be a replacement for Algebra II, or an option preparing students for advanced data science courses and careers. We invite you to preview the materials. If you teach introductory statistics and want to join our project, find out how to use the book with your students. Preview the course here:

https://coursekata.org/preview/default/program

Merge & Purge (UChicago)

This model proposes a "merge and purge" of traditional K-12 mathematics, typically organized into the Algebra I—Geometry—Algebra II sequence. By removing less relevant content, typical topics in Geometry and Algebra II are each condensed to a 1/2-year. The new space would then be filled by integrating a full year of topics in data science, statistics, and mathematical modeling using contemporary computational tools. This teaches some of the same mathematical logic necessary for critical thinking skills, but with more relevant, twenty-first century content. The Center for RISC is currently seeking ambitious schools or school districts to pilot the model, and would be supported by our Center's team for developing and implementing such a program.

Page updated February 2021. Please contact us for more information or questions.

 

Standards & Frameworks

> Guidance for teaching data literacy & data science

GAISE II: Statistics & Data Science Education

The Pre-K–12 (i.e., precollege) Guidelines for Assessment and Instruction in Statistics Education II: A Framework for Statistics and Data Science Education report (GAISE II). This report presents a set of recommendations toward data literacy at the elementary, middle, and high school level. GAISE II (Bargagliotti et al., 2020) incorporates enhancements and new skills needed for making sense of data today while maintaining the spirit of the original Pre-K–12 GAISE (Franklin et al., 2007), which we shall review briefly. Now more than ever, it is essential that all students leave secondary school prepared to live and work in a data-driven world, and this report outlines how to achieve this goal. 

 

Endorsed by the American Statistical Association (ASA) and the National Council of Teachers of Mathematics (NCTM).

California Mathematics Framework

The California Department of Education (CDE), Instructional Quality Commission, and State Board of Education are commencing the revision process for the Mathematics Framework for California Public Schools: Kindergarten Through Grade Twelve (Mathematics Framework).

 

The 2021 Draft Framework now has a full chapter on data science education (see Chapter 5), and is available for receiving public comment through April 2021. We encourage everyone to leave comment by contacting mathframework@cde.ca.gov

K-12 Computer Science Framework

Data education can and should have multiple homes in K-12. Imparting data literacy can be a goal of many other school subjects (mathematics, biology, social studies), and help reinforce content through hands-on data explorations in service of other content standards. Professional, career-track data science often involves some coding, and can be taught within computer science classes. 

The Association for Computing Machinery, Code.org, Computer Science Teachers Association, Cyber Innovation Center, and National Math and Science Initiative have collaborated with states, districts, and the computer science education community to develop conceptual guidelines for computer science education. These guidelines are designed to transform computer science into a subject accessible for all.

Page updated February 2021. Please contact us for more information or questions.

 
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