Network Paradigms for the Learning Sciences: Using Network Science to Close the Gap

 

STEPHEN UZZO

NEW YORK HALL OF SCIENCE

 

Network Science has proven itself to be a powerful paradigm for understanding complexity in dynamic systems. But it may also become an increasingly important approach for the learning science community to help better understand the learning process itself. It may also become an important tool to help close the gap between the way science is done and science learning. The author believes we can advance the science learner’s understanding of complexity through similar kinds of visualization and analysis tools we use on a daily basis in network science. As the role of such things as network visualization and modeling expands within the domains of learning, many opportunities for studying its effect become possible. It is important, though that the learning science, cognition and epistemology research community focus more resources and energy on this aspect of network science. This paper will discuss the problems of learning complex science from a learning science and cognition standpoint, and the potential for network science to address them through powerful visual paradigms and other emerging modalities, such as tangible and interactive modeling. It will provide the theoretical basis for studying the effect of networks on science learning, the approach the author believes will be most fruitful, and some of the research projects that may be of interest to the network research community. It will also discuss aspects of how cyberinfrastructures may be used to develop teaching and research tools for learning science to study the acquisition of complex science ideas by learners at all levels.