Strengthening Data Literacy across the Curriculum (SDLC)

The Strengthening Data Literacy across the Curriculum (SDLC) project is developing and studying high school curriculum modules that integrate social justice topics with statistical data investigations to promote skills and interest in data science among underrepresented groups in STEM.

In today’s data-driven world, innovation, scientific progress, and the health of civil society demand a citizenry and a workforce with strong statistical thinking and data literacy practices. Studies show, however, that schools are neither preparing students adequately nor drawing enough students—particularly from traditionally marginalized groups—to data science fields.

To address this need, the SDLC project is designing and testing a set of curriculum modules that will promote important data practices and build interest in statistics and data analysis among diverse populations of high school students. Targeted toward mathematics and statistics classes that are not at the advanced-placement (AP) level, the modules provide students with opportunities to examine the social and economic conditions of groups in U.S. society using person-level microdata from the American Community Survey (ACS) and the U.S. decennial census.

In one module, titled Investigating Income Inequality in the U.S., students address questions such as: What is income inequality? How have incomes for higher- and lower-income individuals in the U.S. changed over time? How much income inequality currently exists between males and females in the U.S.? Does education help to explain the wage gap between males and females? In another module, Investigating Immigration to the U.S., students explore questions such as: Are there more immigrants in the U.S. today than in previous years? Where have immigrants to the U.S. come from, now and in the past? Are immigrants as likely as the U.S. born to be participating in the labor force, after adjusting for education? View sample lessons: Investigating Immigration to the U.S.: Module Overview and Sample Lessons and Investigating Income Inequality in the U.S.: Module Overview and Sample Lessons.

Students conduct their data analyses using the Common Online Data Analysis Platform (CODAP), an open-source set of tools that supports data visualization and conceptual understanding of statistical ideas over calculations. Lessons encourage collaborative inquiry and provide students with experiences in multivariable analysis—an important domain that is underemphasized in current high school mathematics and statistics curricula but critical for analyzing data in a big-data world.

Over a period of three years, the project is using a mixed methods approach to study three primary research questions:

  • What is the feasibility of implementing SDLC modules, and what supports may teachers and students need to use the modules?
  • In what ways may different features and components of the SDLC modules help to promote positive student learning and interest outcomes?
  • To what extent do students show greater interest in statistics and data analysis, as well as improved understandings of target statistical concepts, after module use?

To investigate these questions, the project has worked with 12 mathematics and six social studies teachers in diverse public high schools Massachusetts and California to conduct iterative research with over 600 students. Through this work, the project aims to build knowledge of curriculum-based approaches that prepare and attract more diverse populations to data science fields.

To learn more, contact Jo Louie at

Project staff

  • Jo Louie, Principal Investigator, EDC
  • Beth Chance, Co-Principal Investigator, California Polytechnic State University
  • Soma Roy, Co-Principal Investigator, California Polytechnic State University
  • Emily Fagan, Senior Curriculum Developer, EDC
  • Jennifer Stiles, Research Associate, EDC
  • William Finzer, Senior Scientist, The Concord Consortium

This project is funded by the National Science Foundation, grant # 1813956. Any opinions, findings, and conclusions or recommendations expressed in these materials are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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