Developing Computational Thinking Through Image Making and Constructionist Learning (Abstract Only)
Eileen Fordham, Halley Freger, Amanda Hinchman-Dominguez, Alexander Mitchell, Daniel Rebelsky, Victoria Tsou, Earnest Wheeler, Zoe Wolter, Samuel A. Rebelsky · 2015
Consider the following scenario: A student is browsing Facebook and sees a strangely compelling image on her friend's page. She follows the "how I made this link" to learn more. She finds herself on a page with a gallery of animations that she browses. Then she sees a "Create" button and clicks it. She skims the instructions and decides that this is not a "standard" graphics program - rather than using a timeline and "tools", it has just a few basic images (e.g., a horizontal blend, a vertical blend, some time-blends) and a few operations that you use to build new images from existing images. She plays a bit. And she finds that she can create some strange and interesting images, but not necessarily the ones she wants to make, and certainly not anything like her friend made. So she looks further. She learns that there are "challenges" that help you learn the system (and, as importantly, that help you consider and master different aspects of computational thinking), and tries to figure out how to make images as seemingly simple as a triangle or a circle using the limited selection of basic images and operations. It's not easy, but she finds it fun to try (and sometimes more fun to fail -- failures also create interesting images). You've just read a sample encounter with the Mathematical Image Synthesis Toolkit, or MIST. MIST is an open-source, Web-based graphics application that takes a constructionist approach in which open experimentation in image making helps students develop skills in computational thinking and deepen their understanding of mathematical functions. MIST is available at http://glimmer.grinnell.edu.