EXP: Linking Complex Systems: Promoting Reasoning within and Across Interconnected Complex Systems

网络学习和未来学习技术计划资助的工作将有助于设想下一代学习技术，并推进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索（EXP）项目通过设计和构建新型学习技术并研究其促进学习的可能性和有效使用它们的挑战来探索新型学习技术的可行性。未来世界的公民和工人必须准备好处理所谓的“邪恶”问题，涉及多个相互关联的复杂系统，包括气候变化、犯罪、传染病、交通等问题。让有背景的学习者准备好面对这些问题是我们这个时代的主要挑战。为了完全理解这样的系统，学习者需要了解系统动态的高级方面和控制其各个交互元素的规则。在这个项目中，康科德联盟、麻省理工学院谢勒教师教育计划和阿贡国家实验室系统科学中心将结合两种不同的经过验证的教育技术，用于理解复杂系统，形成强大的混合技术。与学习者一起使用它并研究它对学习系统动力学的价值，该项目将阐明如何培养对多个相互关联的复杂系统的深刻理解。该项目将与不同的学区进行研究，并将向全国的研究人员和实践者免费提供项目材料和技术。此外，该项目有潜力创建工具，并为目前使用技术建模和理解复杂系统作为其日常工作一部分的大型专业领域产生巨大实用的理解。目前存在两种主要的方法和技术来帮助学习对复杂系统进行推理。系统动力学方法提供了一个广泛的、“鹰眼视角”的系统，它促进了对系统及其组件内部结构和交互的几乎直接的感觉，而基于代理的方法提供了一个“蚂蚁眼视角”，它揭示了系统交互背后的细节和机制。与这两种方法的交互发生在相似的不同粒度上，系统动力学视图提供了快速实例化和轻松重塑组件之间大规模连接的能力，而基于代理的方法提供了“精细控制旋钮”，可以对系统各个参与者背后的复杂规则进行微妙的调整——在复杂系统中，精细的调整通常会导致整个系统本身出现惊人的大而反直觉的变化。如果这些解释层次之间没有明确的联系，学习者就会留下碎片化的体验和理解。该项目将麻省理工学院StarLogo的基于代理的建模能力与Concord Consortium的SageModeler软件的系统建模和绘图能力相结合，将开发一种重要的新型教育技术，称为链接混合建模，并在K-12科学教室中进行测试。这种新的技术类型，能够允许学习者第一次在详细的个人模型和库存和流量的全局视图之间移动，将实现全新的实验模式，并最终培养学习者对复杂系统和系统动态的推理水平，这在目前是不可能的。该项目研究将结合系统动力学和系统涌现的理论框架，应用基于设计的研究方法来研究学生对复杂系统的推理。通过研究这种新类型的使用和设计能力如何导致富有成效的复杂系统推理，从而更好地理解系统，该项目将为理解如何在棘手问题的背景下培养强大的学习奠定基础。

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. Citizens and workers in tomorrow's world must be prepared to approach what have been called "wicked" problems involving multiple, interlinked complex systems, including issues such as climate change, crime, communicable diseases, transportation, and many more. Preparing learners with the background to face such problems is a primary challenge of our age. To gain a complete understanding of such systems, learners need to understand both the high-level aspects of a system's dynamics and the rules that govern its individual interacting elements. In this project, the Concord Consortium, the MIT Scheller Teacher Education Program and the Argonne National Laboratory Systems Science Center will combine two different proven educational technologies used for understanding complex systems to form a powerful hybrid technology. Using it with learners and researching its value for learning about systems dynamics, the project will shed light on how to foster deep understanding of multiple, interlinked complex systems. The project will conduct research with diverse school districts and project materials and technologies will be made available free of charge to both researchers and practitioners nationwide. Additionally, this project has potential to create tools and generate understanding of great utility to the large field of professionals who currently use technology to model and understand complex systems as part of their everyday work.Two main approaches and technologies exist currently to aid learning to reason about complex systems. Systems dynamics approaches offer a broad, "eagle's eye view" of a system that facilitates an almost-immediate sense of the structure and interactions within a system and its components, while agent-based approaches offer an "ant's eye view" that lays bare the details and mechanisms behind the system's interactions. Interactions with these two approaches occur at similarly different grain sizes, with systems dynamics views offering the ability to instantiate and easily recast large-scale connections among components quickly and agent-based approaches offering a "fine-control knob" that enables subtle tweaking of the intricate rules underlying the system's individual actors--fine tweaks that, in complex systems, can often result in surprisingly large and anti-intuitive changes in the overall system itself. Without an explicit connection between these levels of interpretation, learners are left with fragmented experiences and understanding. Merging the agent-based modeling capabilities of MIT's StarLogo with the systems modeling and diagramming capabilities of the Concord Consortium's SageModeler software, the project will develop an important new genre of educational technology termed linked-hybrid modeling and test it in K-12 science classrooms. This new technology genre, capable of permitting learners to move between detailed individual models and global views of stocks and flows for the first time, will enable whole new modes of experimentation and should ultimately foster levels of learner reasoning about complex systems and systems dynamics that are not currently possible. The project research will combine theoretical frameworks for both systems dynamics and systems emergence, applying a design-based research approach to study student reasoning of complex systems. By examining how use and design affordances of this new genre lead to productive complex systems reasoning and thus better understanding of systems, the project will lay the groundwork for understanding how to foster powerful learning in the context of wicked problems.