Feature - NetLogo: A low threshold, no ceiling language
Elementary school students may not be able to decipher mathematical models such as Maxwell’s Equations. But given the right visualization and computational modeling tools, they can learn the underlying concepts.
Meet NetLogo, a multi-agent programmable modeling environment authored in 1999 by Uri Wilensky, a learning sciences and computer science professor at Northwestern University, and founder of the Center for Connected Learning and Computer-Based Modeling.
Remember the turtle?
A generation of adults were introduced to functions and programming through Logo and the “turtle” - an on-screen triangular cursor - that accompanied it. Logo was first created in 1967 by Wally Feurzeig and Seymour Papert at MIT.
The first Logo implementation, Ghost, was written in LISP. But this functional programming language’s intellectual lineage can be traced back to such areas as artificial intelligence, mathematical logic, and developmental psychology.
Wilensky’s NetLogo takes the familiar language to a new level, blending Logo with StarLisp. StarLisp was in turn conceived by Cliff Lasser and Steve Omohundro in 1985 at Thinking Machines Corporation, for use on the company’s supercomputers.
“While in Logo, you can only give commands to program the behavior of a single turtle, in NetLogo, you can create as many turtles as you want, and give them commands to model emergent patterns that arise from the interactions between these agents,” explained Pratim Sengupta, a learning sciences researcher at Vanderbilt University.
From supercomputer to school laptop
Today, NetLogo is in wide use by researchers in the natural and social sciences, according to Wilensky’s biography, and it comes equipped with models in other domains, such as economics, biology, physics, chemistry, psychology, and system dynamics, to name a few.
Sengupta’s group is using NetLogo to study how students learn about complex mathematical and scientific concepts.
To understand electricity from a microscopic perspective, for example, higher-level physics students traditionally use their knowledge of differential equations and integrals.
“That’s real easy at the level of a Ph.D. student, but inaccessible to a fifth grade student,” Sengupta explained. “Fifth graders don’t know algebra very well. They don’t have a handle on what rates are. They don’t know what calculus is.”
That’s where the educational models and modeling labs built in NetLogo by Sengupta’s team come into the picture.
“What the labs do is they show the aggregate level behaviors of electric current as emergent from simple interactions between many individual objects such as electrons and ions,” Sengupta said. “This is a computational narrative of what’s going on, which is both generative and intuitive.”
So far, Sengupta has conducted trials with over 500 fifth, seventh, and twelfth grade students from several different US schools, and researchers in Singapore and Australia are now using NetLogo in ninth through twelfth grades in several schools. All of these studies support the theory that younger students can develop fairly sophisticated forms of scientific thinking and reasoning through bootstrapping their intuitive knowledge - a theory that contradicts the dominant school of thought that has shaped science curriculums around the world.
Sengupta and Wilensky’s work goes beyond providing evidence that could overturn what we thought we knew about teaching science, however. It also identifies a mechanism through which students with no previous exposure to mathematically advanced science can gain an intuitive grasp of aggregate behavior on a microscopic level. And in the process, it leaves behind a tool that novice learners at various ages can use to learn about electricity.
Sengupta hopes to re-examine and push the limits of what students can learn across several domains such as physics, environmental literacy and evolution, by iteratively building and empirically testing more generalizable versions of the cognitive model of naive intuitions on which he has been working on over the past few years. He is also working on creating more agent-based computational programmable toolkits and learning environments that can support longer-term learning progressions, spanning several years.
—Miriam Boon, iSGTW