DIP: Modeling in Levels

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The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and PI teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This project aims to tackle a difficult and important concept for learners at all levels: understanding complex systems. Complex systems are systems with many interacting parts and feedback loops and where the phenomena we experience emerge from the interactions between very large numbers of underlying usually tiny components. Our weather and climate systems, the flight patterns of birds, the circulatory systems in our bodies, and results of predator and prey interactions are all examples of complex systems. The NetLogo system is the premier system used in educational contexts to help learners engage with and understand complex systems; they can use NetLogo both to model such systems and to watch the way such systems behave in different conditions. But it is difficult to conceptualize complex systems so as to be able to model them, and causality and influences underlying "emergence" of the phenomena we can sense are particularly difficult to understand. This team is offering a new way to think about complex systems and software to help with understanding the idea of emergence. They will add multi-level modeling to NETLogo, and their research will focus on how understanding of complex systems develops when software tools that make emergence easier to understand are available and when learners are helped to think about complex systems as having multiple levels. Such understanding is important to understanding the natural phenomena around us as well as the workings of social systems, and in disciplines as diverse as biology and public policy. Understanding complex systems is an imperative in our current society. Putting good social and other public policy into place requires such understanding, as does reasoning about climate and environment. As well, much of modern biology focuses on complex systems and their interactions. The Northwestern team has been a leader in designing software infrastructures for supporting such learning, using as their foundations literature on cocneptual change and learning about systems. In this project, they are extending the modeling approach called agent-based modeling so that it can support multi-level modeling of complex systems. The innovation is two-fold: the design of tools in support of multi-level agent-based modeling of complex systems and support for modeling complex social systems. The hypothesis is that such modeling tools will make it easier for both novice and expert modelers to model and come to understand both particular complex systems and the idea of complex systems. Research questions are grounded in that hypothesis, with the aim to investigate the development of understanding of complex systems when such tools are available and supported well in the surrounding socio-technical environment and the special affordances for fostering such understanding when learners are helped to conceptualize particular complex systems as multi-level (rather than 2-level) systems. The proposed project has the potential to both make the modeling of such systems more accessible and raise the ceiling on what can be modeled qualitatively.