I need to find a better way to explain what I mean when I say, "I want to apply tools useful for understanding and managing natural resources to better understand and manage learning systems." I realize that statement is not at all descriptive to anyone who hasn't done large scale resource management. The name of the technique, "computational modeling," isn't very helpful, either, unless it's already familiar. One effective way to describe a difficult concept is through analogy, and this seems like a good forum to try out some ideas. I hope you'll leave a comment to let me know what you think!
Through the mid 20th century, managing large tracts of land for multiple purposes required a lot of intuition. Imagine being the first Director of the US Forest Service in 1905, charged with providing for recreation, timber, clean water, aesthetics, mineral extraction, fire protection, etc, across all the different types of landscapes in the country. The sheer number of factors that might impact just one part of that landscape is mind boggling. But here is where one of my favorite cognitive tools comes into the story to save the day: maps. Maps can abstract some of the most prominent and crucial landscape features into a form that provides the Big Picture with almost no cognitive effort. This allowed people to dedicate their mental energy to identifying patterns within this big picture. While old paper maps can help frame the overall problem, it was the growth of aerial photography in the early 20th century and then satellite imagery in the latter part of the century that provided land managers a tool for managing on a day-to-day basis using near real-time data about this vast and varied landscape.
Even though maps can help frame the overall area of concern and imagery can give you constant data on the actual characteristics across the entire landscape, land managers still faced an overwhelming number of factors that could impact the land. Even more factors, now, with the inclusion of all this remotely sensed data. Enter computational modeling. With the growth of computing resources, land managers were able to off-load all the factors they knew that might influence the landscape into a computer. They told the computer what they knew or suspected about the on-the-ground impacts of these factors. The computer then did the math on all these assumptions and told the managers which of these assumptions actually manifested in all the real time data coming in and which did not. This allowed land managers to verify and discard their intuitive assumptions, steadily improving our understanding of ecosystem dynamics, how ecosystems change over time based on interactions of impacting factors. These computational models formed the basis of decision support systems, like GIS (geographic information systems). These decision support systems, the ultimate cognitive tool, stored everything that many people knew about the landscape, expressed the confidence level of this knowledge, and allowed each decision maker to optimize decisions based on objectives and risk tolerance.
It is not likely that humans will ever have a computational model that can completely mimic the real world. In The Hitchhiker's Guide to the Galaxy, the computer capable of processing such a model was Earth, itself. This does not diminish the value. Computational modeling is a powerful research and management tool. The gains in our understanding of the landscape and our place in it, environmental science, is founded on computational modeling. This same tool could bring clarity to the vast number of interacting factors that influence learning. Computational modeling could form the basis a cognitive tool to help educators and individuals make decisions to achieve a learning goal in the most effective way for that learner, in that place, at that time.
No comments:
Post a Comment