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Simon Khuvis

Student B.S. Engineering, Cooper Union for the Advancement of Science and Art


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How can computer models help us build intuition?

The use of visual diagrams to explain and understand difficult concepts is as old as history itself, but in the twentieth century, for the first time, engineers and scientists were able to enlist the help of computational tools to represent systems with greater clarity and detail. While computers, with the right peripherals, are able to present data to all the senses, in two or three dimensions and through time, perhaps their greatest pedagogical virtue is their interactivity. People learn by doing: young children internalize Newton's Laws long before their first formal physics class by manipulating the world around them. Computers offer the promise of similar interactivity for systems which are less readily accessible, or even entirely esoteric. In my Bioelectricity class, for example, we have been using computer simulations of the complicated Hodgkin-and-Huxley membrane equations to gain insight into neural reactions to various experimental stimuli. How can computer models be used to learn, understand and ultimately build intuition about systems in nature and science?


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    Feb 15 2012: When it comes to using computers to build intuition and learn about our world, I completely agree that the key factor is interactivity. The young child who learns about gravity by knocking a vase off of a table is essentially performing a science experiment. She has a feeling that maybe something interesting will happen if she hits the vase, and then gives it a try and observes the results. The key thing that makes this type of experiment so effective is the continuous, immediate sensory feedback we get from our environment.

    One of the most powerful things about interactive computational simulation is the ability to perform these same sorts of experiments on all kinds of systems that we can't normally directly manipulate with our hands. But for this type of interactivity to be most successful as a learning tool, its feedback has to be immediate and engage the senses in a rich way. WIthout this rich, immediate feedback, people have a much harder time understanding and internalizing causal relationships.

    Here's something I built to explore how these ideas could be used to teach kids about chemistry:

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