Today, we face the challenges of global climate change-understanding the evidence for it, appreciating its potential impacts, and developing new technologies and policies that can help us adapt to it. A first step toward preparing ourselves to meet these challenges is to ensure that all of our young people get the best possible education in science, mathematics, and technology.
Teaching and Learning with Data
Is teaching with data part of your classroom practice? If you have ever referred to observations, either those of your students', or those of someone else, while in the classroom, you have been teaching with data.
In the Explore section of this professional development experience, you examined data in several different forms. You examined a graphical representation of data showing the impact of climate change; a scientific visualization that provided a map-based picture of the data, and finally, you may have read some text describing the data. Each of these data sets requires a different set of skills on the part of the learner.
Graph reading requires a discrete set of skills. First, the learner needs to understand the conventions of graph design: the use of an x-axis, y-axis, scales, and the practice of connecting or depicting data points. Second, in order to read a graph, the learner needs to know how to make comparisons between the data points, and make approximate calculations by eye based on the data they see. Finally, in order to generalize or identify trends, the learner needs to be able to relate the information in the graph to its real-world context.
The animated scientific visualization, such as NASA's original Sea Level Viewer, is a relatively new instructional tool, and an area of active educational research. It is suggested that scientific visualizations garner their power from integrating data and presenting it as a series of images that mimic the way a brain might organize and image assimilated data on its internal "visual-spatial notepad." The scientific visualization allows the learner to skip the step of data assimilation, and see the results as if they knew all the details of the data set by heart. We know significant scientific discoveries emerged from pictorial thoughts: Einstein himself noted that he rarely thought in words and had trouble translating his mental images into words and equations. Therein rests the possibility that scientific visualizations can jumpstart the creation of new knowledge.
You can assist students in their interpretation of data visualizations by providing scaffolding for their exploration. Begin by asking students questions such as:
- "What does the x-axis represent?"
- "What does the y-axis represent?"
- "What is the shape of the curve/trend of the line?"