Argonne Education awarded NSF grant for atmospheric science curriculum
Argonne National Laboratory’s Education Department is part of a four-institution partnership that was awarded $2.4 million over three years by the National Science Foundation (NSF) to develop a curricular unit on computational weather forecasting. Students using this middle school curriculum will explore weather phenomena the same way as an atmospheric scientist through an approach called embedded phenomena.
With their classroom as their laboratory, students will be immersed in and visually experience a virtual storm front coming through their classroom. Through the use of classroom sensors, handheld devices and whole-classroom displays, students will collect data on this phenomena and then use modeling environments to help them forecast where the front is heading and when the next front will be coming. This curriculum will integrate cutting-edge modeling and visualization technologies and innovated pedagogy with expert atmospheric science.
Led by investigators at Concord Consortium, the project team includes weather scientists, computer scientists, education developers and learning scientists from Argonne National Laboratory, Millersville University and the University of Illinois at Chicago. This national opportunity leverages Argonne’s expertise in atmospheric science, visualization and data analysis.
“So much of atmospheric science is modeling and predicting, yet the current teaching of weather in middle school is limited to making observations and memorization. This project will provide students an opportunity to become an atmospheric scientist and practice those important computational thinking skills that atmospheric scientists use all the time,” explains Meridith Bruozas, manager of Education and Outreach and Argonne lead on this project. Atmospheric scientist, Scott Collis (EVS) and Lead Visualization and Data Scientist, Joseph Insley (LCF) are members of the design and development team. Argonne is home to the sixth fastest supercomputer in the world and one of the founders of the Atmospheric Radiation Measurement (ARM) Climate Research Facility.
The curriculum consists of instructional materials and technologies that transform classrooms into dynamic weather simulations that scaffolds students’ learning and use of science, mathematics and computational thinking as they (a) collect and analyze data from the simulated weather events; (b) develop and refine computational models from these data; (c) and use computational models to make and evaluate weather predictions. Live webcasts with Argonne meteorologists enable students to learn about how they made predictions from same data sets students examined. Over three years the project will engage eight teachers and their 430 students who will work with the project team members to test the curriculum in distinctive middle school settings in Illinois, Massachusetts and Alaska. The curriculum will be licensed via open source and open content licenses and freely distributed to other teachers, curriculum designers and researchers through Concord Consortium’s website.
Students in a Precipitating Change classroom hear a rumbling, and they immediately spring to their feet. One team debates where to place the radar arrays this time, noting that last time they were too far apart to get a strong signal at the storm’s center. A second team gathers in a corner, studying a set of “satellite photos” they have just received from the classroom weather simulation server. Students actively debate sampling rates and signal strengths as they consult the image. Two banks of clouds appear to be converging on the room’s diagonal. An updated satellite photo arrives, confirming their suspicions — the storm is focusing right in front of the teacher’s desk, and it looks like a big one! The radar team moves quickly, placing their radar arrays in a triangle at three sides of the room. They all look to a large flat-screen monitor at the side of the room, listening to the thunder get louder and watching the data roll in as the storm “passes through.”
The room is quiet — the storm has passed. But the students are excited, knowing that now their work really begins. They grab the tablets and look to the monitors on the walls. The satellite photo group has the bird’s eye view, but the students know that can be deceptive — satellite images show clouds well, but there is plenty these images don’t reveal. Knowing that the satellite views don’t provide any information about where the storm is strongest or how it is evolving, the student teams set about parsing and merging all three of their data sources to provide the integrated story they require.
The radar team jumps to pore over their data, burying themselves in the details of a freshly generated set of colorful images, and juggling them against output from multiple computational models. Intense discussions about model weaknesses and strengths fill the room as the teams consider how to best combine the new radar trace with these models. Then, a debate — is that red patch a flock of birds or a strong storm signal? They’ll need to talk to the ground team. They have the most detailed story, essential for making sense of the other teams’ visualizations. The radar and ground teams work together, viewing their data on a monitor on the side wall, and pointing and gesturing at two distinct red patches. The team traces coordinates. They create and run ground data search loops. They hurry to process results. This storm is a complicated one, requiring them to draw upon all the data analysis and computational modeling skills they have been building over the past few weeks. And an answer: Only one of the two red areas corresponds to ground measurements indicating high rainfall speeds and lower temperatures as the storm passed over. The other is a false signal, likely generated by a flock of passing birds flying under the clouds and thus not present in the satellite team’s view.
Armed with this information, the teams pull their data together. They can now say with high confidence that the storm center passed through the front right corner of the classroom, traveling approximately 30 miles per hour.
National Science Foundation: STEM+Computing Partnerships (STEM+C)
Computing and computational thinking are an integral part of everyday practice within modern fields of science, technology, engineering and math (STEM). As a result, the STEM+Computing Partnerships (STEM+C) program seeks to advance new multidisciplinary approaches to the integration of computing in STEM teaching and learning, and discipline-specific efforts in computing designed to build an evidence base for teaching and learning of computer science in K-12 and within diverse populations.