As I wrote in my first blog about Educon 2.1, a school reform conference with a strong technology bent, I attended a pre-conference workshop called, “Constructing Modern Math/Science Knowledge.” As an educator with a strong constructivist bent I was eager to hear Gary Stager, Brian Silverman (Picocrickets), Carolyn Staudt (Molecular Workbench), and others discuss how computers can function as learning material. This is different from technology integration, which is the application of technologies to enhance learning of subject matter (and where most teacher-planning goes). Constructivist theory is more along the lines of helping kids manipulate or even create the instruments that allow them to probe into and answer unique questions. Stager, Silverman, and the rest made it clear how invention, creativity, and inquiry are all brought into play when kids operate in programming and modeling environments, but it’s a hard sell to teachers who have little wiggle room in their content-crowded, heavily-tested curriculum. What to do?
During David Thornburg’s breakout session we came close to connecting the dots between computational thinking and concerns over subject matter. He cast the issue in terms we teachers understand, and that is, How do we design inquiry-driven projects and integrate science, technology, engineering, and math (STEM) disciplines? Though he didn’t bring up the term computational science (that sounds scary and we were already deep in the weeds), it’s in there, especially when kids have to drill down to ask questions that are at the heart of computational thinking in order to solve a problem: Can I ask a question in such a way that it can be solved with the aid of computers? What information do I need? What can I know through computation? What can’t I know?
As Peter Denning and Jeanette Wing tell us, it’s at the intersection between an inquiring human’s imagination and a computer’s capability where computational science takes off. Dr. Thornburg had us form into small groups and gave us this prompt: Come up with a recommendation for one solution for these three problems: A collapsing economy, fuel scarcity, and global climate change. Each of the five or six groups came up with a unique solution, all involving STEM, and in every instance involving innovative research and development that would call on computational science.
Of course the example was massive, but it made the point: solutions to complex, real-world problems require an integrated approach, projects in school should integrate STEM, and such integration naturally draws on and extends computational thinking.
Thornburg’s workshop was particularly helpful because the next day I was scheduled to lead a session with my colleague Suzie Boss that similarly illustrated how, on a more manageable school scale, project-based learning with technology can go beyond subject matter understanding so kids actually make a difference in the world. Our touchstone example was the Black Cloud Project, where high school students in south central Los Angeles operate as “citizen scientists” as they tackle air quality problems in their neighborhood. Their teacher Antero Garcia collaborated with Dr. Greg Neimeyer at University of California at Berkeley to place sensors, which test light, noise, temperature, CO2 and volatile organic compounds, in strategic places throughout the neighborhood. (This video describes the project.) How deeply they got into the design of the sensors I don’t know, but as Mr. Garcia describes it, since using them students have begun to wonder what other phenomena they can capture for study. That sounds like computational thinking to me.
Read my previous installment of this blog series, Educon 2.1: Eye of the Tiger, as well as my last installment, Educon 2.1:What Does Reform Look Like?
