Writing Data Stories: Integrating Computational Data Investigations into the Middle School Science Classroom

Preparing today’s students to work with data fluently is critical to ensuring a scientifically literate and empowered citizenry. But most efforts to incorporate data analysis into the K-12 curriculum are limited to short, isolated activities; basic skills development; or, are introduced as new courses devoted specifically to data and computing, limiting both the potential benefits and the audience for such efforts. The Data Stories project (2019-2022) brings together a team of researchers from University of California Berkeley, NC State University, and Concord Consortium to integrate computational data analysis into the middle school science curriculum in a longitudinal, interdisciplinary way – drawing from the computer and data sciences, literacy studies, statistics, and science education to saturate the classroom with relevant tools, resources, and support.

Middle school classrooms, in the San Francisco CA area, will analyze and draw conclusions about publicly available scientific datasets using a free, innovative, computational data analysis platform called the Common Online Data Analysis Platform (CODAP). Units will be designed specifically with Dual Language Learners (DLL) in mind, inviting students to share their investigations by writing multimodal texts that blend both familiar and academic modes of expression to explain and contextualize their data analysis processes. Units that build on one another in difficulty and complexity will be introduced throughout the academic year, and participating teachers will receive significant training opportunities. Overall the project is anticipated to directly impact approximately 2,500 students and 20 teachers in the greater San Francisco Bay area, from predominantly high needs schools.

The project will provide a research context to address the following questions:

  • How do students learn, over time, to use computational tools to structure, calculate, filter, and transform data for scientific inquiry?
  • What patterns of engagement in scientific practices are supported by the integration of computational data analysis and visualizations into the science curriculum?
  • What new literacy practices might support DLL and learners with limited access to technology or who are still developing academic literacy in constructing oral and written arguments and explanations using data and visualizations as evidence?

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