|Interactive science (Image credit: Franco Landriscina)|
If future historians were to write a book about the most important contributions of technology to improving science education, it would be hard for them to skip computer modeling and simulation.
Much of our intelligence as humans originates from our ability to run mental simulations or thought experiments in our mind to decide whether it would be a good idea to do something or not to do something. We are able to do this because we have already acquired some basic ideas or mental models that can be applied to new situations. But how do we get those ideas in the first place? Sometimes we learn from our experiences. Sometimes we learn from listening to someone. Now, we can learn from computer simulation, which was carefully programmed by someone who knows the subject matter well and is typically expressed by a computer through interactive visualization based on some sort of calculation. In the cases when the subject matter is entirely alien to students such as atoms and molecules, computer simulation is perhaps the most effective form of instruction. Given the importance of mental simulation in scientific reasoning, there is no doubt that computer simulation, bearing some similarity with mental simulation, should have great potential in fostering learning.
|Constructive science (Image credit: Franco Landriscina)|
Although enough ink has been spilled on this topic and many thoughts have existed in various forms for decades, I found the book “Simulation and Learning: A Model-Centered Approach” by Dr. Franco Landriscina, an experimental psychologist in Italy, is a masterpiece that I must have on my desk and chew over from time to time. What Dr. Landriscina has accomplished in a book less than 250 pages is amazingly deep and wide. He starts with fundamental questions in cognition and learning that are related to simulation-based instruction. He then gradually builds a solid theoretical foundation for understanding why computer simulation can help people learn and think by grounding cognition in the interplay between mental simulation (internal) and computer simulation (external). This intimate coupling of internalization and externalization leads to some insights as for how the effectiveness of computer simulation as an instructional tool can be maximized in various cases. For example, Landriscina’s two illustrations, embedded in this blog post, represent how two ways of using simulations in learning, which I coined as “Interactive Science” and “Constructive Science,” differ in terms of the relationships among the foundational components in cognition and simulation.
This book is not only useful to researchers. Developers should benefit from reading it, too. Developers tend to create educational tools and materials based on the learning goals set by some education standards, with less consideration on how complex learning actually happens through interaction and cognition in reality. This succinct book should provide a comprehensive, insightful, and intriguing guide for those developers who would like to understand more deeply about simulation-based learning in order to create more effective educational simulations.