Contextualizing Data Education via Project-Based Learning

Integrating rich data experiences into interdisciplinary project-based learning


Data fills all aspects of our lives, and data science has become a vital interdisciplinary endeavor. Data is valuable because of the insights it can provide into real-world problems and situations. To succeed in the future, students must come to see data as a tool they can wield to similarly useful ends. Students need opportunities to apply data science practices to realistic datasets to address issues within contexts relevant to their lives and to exhibit their understanding and skill through rich, authentic tasks.

Interdisciplinary project-based learning (PBL) offers great promise for just such authentic, contextualized data education. Engaging and empowering students with the responsibility and opportunities to apply “messy” datasets to relevant societal issues can help them develop acumen with data and agency as learners. Engaging with problems across disciplines can help students see data’s relevance to addressing issues in their futures.

This project will enhance two open-resource EL Education PBL modules with authentic data experiences. Co-designing these modules with middle school teachers, we will examine how interdisciplinary data education can provide opportunities for marginalized students to take more control of their own learning and develop positive identities related to data, through integration with social studies and science topics.


This project addresses four key research questions about data and learning:

  • In implementations of the DataPBL curriculum, what interdisciplinary data practices do students participate in, and under what conditions?
  • Under what conditions do students manifest agency in the course of their data-infused PBL?
  • How do aspects of the experienced projects contribute to developing positive identities related to data?
  • How can teachers integrate data practices into interdisciplinary project-based learning curriculum modules?


Blog Posts

Learn more about the DataPBL project at the Concord Consortium blog.

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Project Funder
This material is based upon work supported by the George Lucas Educational Foundation and the National Science Foundation under Grant No. DRL-2200887. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funders.
Principal Investigator
Chad Dorsey, Joe Polman, Ron Berger, Linda Grein
Years Active