What’s next for these learning progressions
Subject-by-Subject Articulations Arriving in 2026
Finding a Home for Data Science
One of the greatest challenges of creating the Data Literacy and Data Science Learning Progressions was deciding how to integrate these skills and techniques across all disciplines. In academia and industry, Data Science is an interdisciplinary subject that combines statistics and mathematics, computer science, and domain knowledge to derive insights from data. Data Literacy is increasingly a non-negotiable basic skill required for decision-making in both professional and personal contexts today. At the most advanced levels, Data Scientists are the primary leads who train AI models and structure data in order to do so – which itself is impacting nearly every discipline. Yet in K-12 education, school subjects remain divided into discrete silos, creating a clear mismatch between the interdisciplinary nature of data science and the traditional structure of school subjects. So how should the subjects of K-12 wrestle with an interdisciplinary topic that moves between traditional academic boundaries?
The answer to this question should not simply be adding data science and data literacy everywhere! That approach, while well intentioned, leaves classroom educators and school leaders uncertain on what to prioritize, where to focus, and who should take leadership for these topics. While we strongly believe data analysis activities and techniques can be a connecting bridge between school subjects in the digital age, the
Subject-by-Subject Timeline
Over the next two years, the DSLP project will work with three priority school subjects for integration, adding some new content knowledge, and to a greater extent providing problem-solving techniques to enrich the way we learn content that is already there. We assume K-12 Mathematics, Science, and Social Studies are the priority disciplines for integration of data analysis techniques and problem-solving methods. Work in Computer Science and English will align additional techniques, with CS providing an even deeper technology toolkit and ELA providing deep engagement with clearer communication techniques for technical or quantitative ideas.
This work will build on earlier efforts to integrate computational thinking across the K-12 curriculum, but with a sharper focus on using subject-relevant datasets to create meaningful, discipline-specific learning experiences in each subject. Picture exploring ecosystem data in biology, analyzing historical unemployment data in history or economics, and using real-life exponential or logarithmic trends for demonstrating mathematical functions with data.
The goal is simple: make each subject’s content come alive and jump off the page with authentic, real-world data-driven activities. With this approach, students will learn formal analysis techniques naturally as they solve real problems, rather than through disconnected drills, ensuring relevance is built into both the subject and the learning experience. By leveraging data science as a teaching strategy to cover existing content in some subjects, it can also enhance rather than replace existing grade-level content.
Between now and December 2026, working groups representing each school subject will meet to select priority DSLP concepts for their discipline, add any missing topics from the base subject-neutral version, and articulate grade-by-grade learning targets. This process will mirror the development of the initial subject-neutral version of DSLP, in collaboration with NCTM, NCSS, NSTA, NCTE, CSTA, and other K-12 associations. If you want to get involved, please express interest here.
If your state or school is considering implementing before these articulations are available, please consider contacting our 1-800-Helpline equivalent for data science education at info@datascience4everyone.org.
Interdisciplinary Inputs, Tailored Outputs
To avoid re-siloing the curriculum, the DSLP project will work to ensure that a certain number of “core” or “essential” topics intentionally repeat in every subject, so that a student encounters the same idea at the same time and grade-level through different disciplinary lenses.
In the status quo, many topics are misaligned across school subjects in middle and high school. In this new approach, the ideas of “correlation vs. causation” may appear from a statistical lens in math class, but will also show up in the context of a misinterpreted historical event in social studies, and as a faulty experiment conclusion in environmental science. We hope this approach will empower more students to be confident in data, even if they don’t view themselves as a “math person,” because it shows up in areas more deeply connected with their interests. We also hope this approach will make the lives of K-5 educators easier, where one person is responsible for every subject concurrently, but where the ideas often stack poorly.
The subject-neutral version of DSLP was also interdisciplinary from Day 1. To build the inaugural progressions, the DSLP project facilitated dedicated focus groups and public voting with classroom educators in mathematics (in collaboration with NCTM), science (in collaboration with the NSTA), social studies (in collaboration with NCSS), computer science (in collaboration with CSTA), and English (in collaboration with NCTE), in addition to higher-education faculty, policymakers, and current students from across disciplines and post-secondary majors. These inputs led to a variety of ideas incorporated into the Top 25 Learning Outcomes for a “Portrait of a Graduate” in the Data & AI era.
Amendments over Time
We don’t believe we answered every question or perfected every learning trajectory in Stage 1. While we built upon over a decade of research and model frameworks in K-12 statistics, computer science, and data science work, there is still much to do. Moreover, emerging technology (including but not limited to AI) creates an additional moving-target challenge for the sector. Prior frameworks such as Common Core State Standards (CCSS) or the Next-Generation Science Standards (NGSS) were not designed to update over time, creating inflexible frameworks for a changing world and cutting off the potential to incorporate future research on how students learn. While these design choices may have been considered sensible if not necessary at the time, they concurrently created the risk that full rebuilds would be needed later on.
The DSLP project will introduce a recurring amendment process so that the K-12 Learning Progressions become a “living document”. Our goal is for smaller but more frequent incremental changes over time. Rather than modifying 50% or entirely replacing an outdated framework, we intend to modify 10% or 15% every few years, as societal needs and research dictate. The first amendment cycle will occur in 2027 for the subject-neutral framework. Changes to subject-specific frameworks will then occur in 2028, incorporating these smaller changes as relevant for each subject.
In some years, subject-specific updates won’t be required at all, or may be especially minor. Amendments will require evidence from both research and classroom experiences, will be voted upon by community members, and will be incorporated by an expert committee to ensure coherence with other parts of the DS Learning Progressions. If you are interested in serving in the Community Amendment Process, please consider applying here.