DIP: Modeling in Levels

DIP：层次建模

• 批准号: 1441552
• 负责人: Uri Wilensky
• 金额: $ 125.07万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1441552&HistoricalAwards=false
• 关键词: DIP; Modeling; Levels

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.

网络学习和未来学习技术计划资助支持展望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。开发和实施 (DIP) 项目建立在概念验证工作的基础上，展示了所提议的新型学习技术的可能性，PI 团队构建并完善了其提议的创新的最低限度可行的示例，使他们能够了解未来应如何设计和使用此类技术，并允许他们回答有关人们如何学习、如何促进或评估学习和/或如何设计学习的问题。 该项目旨在为各个级别的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循环的系统，我们所经历的现象是由大量底层（通常是微小的）组件之间的相互作用产生的。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 系统是教育环境中使用的首要系统，可帮助学习者参与和理解复杂的系统；他们可以使用 NetLogo 对此类系统进行建模并观察此类系统在不同条件下的行为方式。但是，很难概念化复杂的系统以便能够对其进行建模，而且我们可以感觉到的现象“出现”背后的因果关系和影响尤其难以理解。该团队提供了一种思考复杂系统和软件的新方法，以帮助理解涌现的想法。他们将在 NETLogo 中添加多级建模，他们的研究将集中于当可以使用使涌现更容易理解的软件工具以及当帮助学习者将复杂系统视为具有多个级别时，对复杂系统的理解如何发展。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不同学科非常重要。了解复杂系统是当今社会的当务之急。制定良好的社会和其他公共政策需要这种理解，就像对气候和环境的推理一样。此外，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。西北大学团队在设计支持此类学习的软件基础设施方面一直处于领先地位，以概念变革和学习系统的文献为基础。在这个项目中，他们正在扩展称为基于代理的建模的建模方法，以便它可以支持复杂系统的多级建模。创新有两个方面：支持复杂系统的多级基于代理建模的工具设计以及支持复杂社会系统建模。假设这样的建模工具将使新手和专家建模者更容易建模并理解特定的复杂系统和复杂系统的想法。研究问题以该假设为基础，旨在调查当这些工具可用并在周围的社会技术环境中得到良好支持时对复杂系统的理解的发展，以及当帮助学习者将特定的复杂系统概念化为多级（而不是两级）系统时促进这种理解的特殊可供性。拟议的项目有可能使此类系统的建模变得更容易，并提高定性建模的上限。