Strategies: Understanding Weather Extremes with Big Data: Inspiring Rural Youth in Data Science

Education Development Center, Waltham MA

Investigators

Abstract

This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM). Advances in computing technology have ushered in an era of big data in science. As scientists increasingly rely on large, professionally collected data sets for scientific discovery, there is a critical need to build a scientific workforce with robust skills in making sense of multivariate data. The project will develop and research four model curriculum units and an interactive experience with weather scientists to promote important scientific data practices and interest in big data science careers among middle-school students in underserved New England rural areas. Designed as 2-week data investigations, the units will provide students with hands-on opportunities to analyze and model large-scale weather data collected from the summit and lower elevations of Mt. Washington, NH (the highest mountain in the eastern United States), as well as data collected from students' local weather stations. Students will learn to describe, explain, model, and predict extreme weather events both in their local settings as well as at Mount Washington Observatory, a site that has recorded some of the most extreme weather conditions in the world. Students will conduct these activities using the Common Online Data Analysis Platform (CODAP) and SageModeler, two online resources developed with NSF funding to support students in middle school and beyond with visualizing, modeling, and analyzing large-scale scientific data. They will also learn from and interact with weather scientists through a virtual Chat with a scientist to deepen their understandings of their own data investigations and to gain insights into scientific careers that use big data. The project will disseminate research findings and educational resources through a variety of channels to reach a diverse and cross-disciplinary group of researchers, educators, and members of the general public. Project findings and resources will be made available through public education websites, conference presentations, peer-reviewed and practitioner publications, social media, and further dissemination through conventional media including print, radio, and television. The project will develop a new multi-faceted set of science learning resources targeted to rural, underserved middle-school students and research the potential of these experiences to promote these students' interests and competence in scientific data analysis and modeling. The project team will collect a variety of qualitative and quantitative data during each phase to inform unit development and to address the project?s research questions. During the early feedback phase, the team will explore the feasibility of using WeatherX units by sharing initial unit ideas and activities with teachers and soliciting their feedback through written surveys and focus-groups During the alpha testing phases, the team will continue to explore the feasibility of using WeatherX units as teachers implement the units with students. The team will attend to any variations in how teachers enact the units and the ways in which the units may support student interest and engagement in data analysis, modeling, and scientific careers. To address these questions, the project team will collect survey and focus-group data from teachers and additional data through teacher implementation logs, teacher individual interviews, classroom observations, student surveys, student focus-groups and student work samples. During the later testing phases, the project team will focus less on feasibility of unit use and more 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, modeling, and scientific careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.