WeatherX is developing middle-school curriculum units that investigate extreme weather events to promote skills in analyzing large-scale scientific data and interest in data science careers among students in rural areas.

As scientific discovery becomes ever more data-driven, there is a critical need to build a scientific workforce with robust skills in large-scale data analysis. Nowhere is the need to strengthen learning opportunities greater than in rural areas, where a majority of the nation’s school districts reside and where under-investment persists.

To promote interests in data analysis and data science careers, WeatherX curriculum units support student investigations of extreme weather events on Mount Washington, NH—often called the “Home of the World’s Worst Weather”—as well as in their local communities. Students use the data visualization and online modeling tools Common Online Data Analysis Platform (CODAP)  as well as large-scale data from the Mount Washington Observatory (MWO) and the National Center for Environmental Information (NCEI) to conduct their investigations.

Over a period of three years, the project is developing and testing a series of model curriculum units, each designed as extended sets of interactive science investigations for middle-school science classrooms. In this effort, the WeatherX team has collaborated with 12 middle-school science teachers and over 470 of their students in low-income rural school districts in northern New Hampshire and Maine. Project research addresses the following questions:

  • What is the feasibility of using WeatherX units in participating classrooms?
  • How do teachers enact WeatherX units?
  • What are the mechanisms by which WeatherX units and their enacted components may have an impact on student learning and interests?
  • To what extent do students who work through WeatherX units show improved understandings of, abilities in, and interest in scientific data analysis and data science careers? 

Key Curriculum Components:

  • A focus on analyzing, describing, and explaining typical and extreme weather events on Mount Washington and in students' local communities
  • Interactive investigations of weather data from MWO and NCEI, using CODAP
  • Activities in which students connect with community members about the local weather, to strengthen the cultural relevance of students' learning
  • Opportunities to learn from MWO weather scientists through video live sessions to build understanding of and interest in data science careers

Unit development and project research has unfolded in a series of iterative phases. During an initial development and feedback phase, the project focused on sharing early unit ideas and activities with teachers and soliciting their feedback. During an “alpha” development and testing phase, the project explored the feasibility of unit implementation, how teachers enacted the units, and the ways in which the units may support student interest and engagement in data analysis and scientific careers. During a “beta” development and testing phase, the project focused on unit enactment, mechanisms by which the units may support student learning, and quantitative measures of improvement in student learning and interest in data analysis and scientific careers. Research during these alpha and beta phases involved collection and analysis of survey and focus-group interview data from teachers and students, classroom observation data, samples of student work, and pre- and post-unit student assessment data.

To learn more, contact Jo Louie at

Project Staff

  • Josephine Louie (Project Lead), EDC
  • Brian Fitzgerald, Mount Washington Observatory
  • Asli Sezen-Barrie, University of Maine
  • Emily Fagan EDC
  • Kevin Waterman, EDC
  • Pamela Buffington, EDC
  • Emily Fagan, EDC
  • Brianna Roche, EDC
  • Deb Morrison, University of Washington
  • William Finzer, The Concord Consortium
  • Sarah Jessen (Evaluator)


This project is funded by the National Science Foundation, grant # 1850447. 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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