Use the Data Literacy Cube to guide students’ exploration of data to enrich their observations and inferences. This is a flexible resource that may be used with a variety of graphical representations of data. This activity requires a graph for students to evaluate. Fo
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In this activity, we will introduce children to the colors of the sky. Children love to look at clouds. Here we will focus in on the sky in which clouds float. Children will learn why the sky has such a wide range of colors.
The purpose of this activity is to have students use an Earth Systems perspective to identify the various causes associated with changes to Earth's forests as they review Landsat imagery of site locations from around the world.
In this NASA investigation, "What's Hot at the Mall," students examine how shopping malls change natural environments by examining thermal images gathered by NASA showing an area in Huntsville, Alabama.
Using hourly graphs of PM 2.5 data and HYSPLIT model trajectories, students will collect evidence for the effects of fireworks on air quality.
Students identify and classify kinds of land cover (such as vegetation, urban areas, water, and bare soil) in Landsat satellite images of Phoenix, Arizona taken in 1984 and 2018.
This Lesson Plan provides some generic maps, graphs, and data tables for use with the Data Literacy Cube. Because this is a differentiated resource, it is appropriate for multiple grade bands.
This lesson, "Awenasa Goes to Camp!," is a data analysis activity that presents maps of NASA Earth satellite data for a variety of locations across the United States for four unidentified months throughout the year. Each location represents a real science camp th
Learners follow adventurous camper, Awenasa, as she travels the United States and attends various camp locations throughout the year. Learners analyze data to find her location among the various campsites using monthly averaged NASA satellite data (Cloud Coverage, Surface (S
Students analyze the data and details of a complicated graph by identifying components and data patterns.