DIP: Modeling in Levels

网络学习和未来学习技术计划为支持设想学习技术的未来并推进我们对人们如何在技术丰富的环境中学习的了解的努力提供资金。开发和实施（DIP）项目建立在概念验证工作的基础上，展示了拟议的新型学习技术的可能性，PI团队建立并完善了他们提出的创新的最低可行性示例，使他们能够理解未来应该如何设计和使用这种技术，并使他们能够回答有关人们如何学习，如何促进或评估学习，and/or how to design设计for learning学习. 该项目旨在为各级学习者解决一个困难而重要的概念：理解复杂系统。复杂系统是具有许多相互作用的部分和反馈回路的系统，我们所经历的现象来自大量潜在的通常是微小的组件之间的相互作用。我们的天气和气候系统，鸟类的飞行模式，我们体内的循环系统，以及捕食者和猎物相互作用的结果都是复杂系统的例子。NetLogo系统是在教育环境中使用的首要系统，以帮助学习者参与和理解复杂的系统;他们可以使用NetLogo来模拟这些系统，并观察这些系统在不同条件下的行为方式。但是，很难对复杂系统进行概念化，以便能够对它们进行建模，而我们能够感觉到的现象的“出现”背后的因果关系和影响尤其难以理解。这个团队提供了一种思考复杂系统和软件的新方法，以帮助理解涌现的概念。他们将在NETLogo中添加多层次建模，他们的研究将集中在如何理解复杂系统的发展，当软件工具，使出现更容易理解可用时，以及当学习者被帮助思考复杂系统具有多个层次。这种理解对于理解我们周围的自然现象以及社会系统的运作以及生物学和公共政策等学科都很重要。理解复杂系统是我们当前社会的当务之急。实施良好的社交及其他公共政策需要有这样的理解，对气候和环境的推理也是如此。同样，现代生物学的大部分内容都集中在复杂系统及其相互作用上。西北大学的团队一直是设计支持这种学习的软件基础设施的领导者，他们使用关于气候变化和学习系统的基础文献。在这个项目中，他们正在扩展称为基于代理的建模方法，使其能够支持复杂系统的多层次建模。创新是双重的：设计工具，支持复杂系统的多层次基于代理的建模，并支持复杂社会系统的建模。假设是，这样的建模工具将使新手和专家建模者更容易建模，并开始理解特定的复杂系统和复杂系统的想法。研究问题是基于这一假设，目的是调查复杂系统的理解的发展时，这些工具是可用的，并支持在周围的社会技术环境和特殊的启示，促进这种理解时，帮助学习者概念化特定的复杂系统作为多层次（而不是2级）系统。拟议的项目有可能使这些系统的建模更容易获得，并提高什么可以定性建模的上限。

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.