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Learning to Learn from Data


Posted: Feb 15 2011 by Kim Kastens
Topics: Interpretation/Inference, Spatial Thinking, Temporal Thinking, Field-Based Learning, Quantitative Thinking, Metacognition
Scientists learn from data. Learning to learn from data is obviously an essential aspect of the education of a future scientist.

These days, however, many other kinds of people also learn from data–including business people, investors, education leaders, and people who care about pollution, disease, or the quality of their local schools. My daily newspaper is rich in data-based graphs and maps–and so is the newsletter from my local library. These days, learning to learn from data is a necessary part of everyone's education.

However, learning to learn from data is not a typical part of everyone's education. This post explores what might be required to construct a thorough learning progression for learning from Earth Science data, beginning where a good elementary school leaves off and carrying on through to what an upper level college course or adult job might demand. More

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Kim's First Lesson on Learning Goals


Posted: Jun 25 2010 by Kim Kastens
Topics: Metacognition, Quantitative Thinking
Last week, my daughter Dana took the Algebra Regents Exam, her first encounter with the Regents system. New York State has had state-set exams for high school academic subjects since my mother was in high school, and they are generally accepted rather matter-of-factly here, with little of the flap that seems to have accompanied the advent of state curricula and state testing elsewhere in the country.

Dana's right of passage got me thinking about my own encounter with the Algebra Regents, in June 1968, at Hamilton High School. Our teacher taught us methodically and conscientiously throughout the school year, and then spent about two weeks reviewing for the Regents. The last day of class, out of the blue, he announced that he was going to teach us one more thing, something that he hadn't taught us during the year. More

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Data-Driven Versus Concept-Driven Visualizations


Posted: Dec 1 2009 by Kim Kastens
Topics: Quantitative Thinking, Perception/Observation, Interpretation/Inference

In a much underappreciated paper, Aaron C. Clark & Eric N. Wiebe of North Carolina State University draw a distinction that should be front and center in the minds of every person who teaches with or learns from scientific visualizations: a distinction between what they call "concept-driven" and "data-driven" visualizations.

In creating a visualization, the initial design is typically driven by classifying graphics into two major categories. ... A concept-driven visualization is typically generated from a concept or theory and not directly tied to any empirical data. It does not mean that there isn't any data that either supports or refutes the theory, but this particular exploration does not require [data.] ... A data-driven visualization uses empirically or mathematically derived data values to formulate the visualization. In this case, a specific relationship between data values and the graphic elements is defined so that a graphic characteristic varies in some predetermined fashion. (Clark & Wiebe, 2000, p. 28.)

From the point of view of a teacher or learner, data-driven and concept-driven visualizations have different affordances and different pitfalls. More

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Turning Nature into Numbers


Posted: Oct 26 2009 by Kim Kastens
Topics: Quantitative Thinking, Perception/Observation

Montage of 350 demonstrators Supporters of 350ppm target for atm. C02 <source>

On October 24, 2009, environmental activists around the world gathered in support of a geophysical data point. With their bodies, banners, and balloons, they formed the numeral 350, advocating that governments should adopt 350 ppm as a target for atmospheric carbon dioxide concentration. How remarkable that a number should have such rallying power. How remarkable that humans are able to conceptualize the invisible stuff we live within and breathe into our bodies as a substance made of numbers. Our ability to do so is an end product of a long series of insights and inventions by our scientific predecessors.

For hundreds of year, a major activity and accomplishment of Earth Scientists and our predecessor natural historians, has been to turn experienced reality into numbers. Earth Science is sometimes dismissed as merely a "descriptive" or "observational" science, but such an attitude understates both the vastness and the power (and the pitfalls) of the enterprise of mathematicizing that which had previously only been known through non-quantitative human senses.

I can jump into a pond or the ocean and sense its temperature with sensors in my skin and describe my sensation in words: "Warm," "Not so cold, come on in," "Cold," "Icey." With the invention of the thermometer, approximately 400 years ago, it became possible to turn these feelings into numbers. Over the same 400 year stretch of time, many attributes and processes of the Earth were turned into numbers: the power of an earthquake, the height of a mountain, the swiftness of the wind, the saltiness of the ocean, the density of minerals, etc., etc.

Why was this considered a good thing to do? More

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