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

EXP：链接复杂系统：促进互连复杂系统内部和之间的推理

• 批准号: 1629526
• 负责人: Carolyn Staudt
• 金额: $ 54.14万
• 依托单位: Concord Consortium
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629526&HistoricalAwards=false
• 关键词: EXP; Linking; Complex; Systems; Promoting

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.

网络学习和未来学习技术计划资助的努力将有助于设想下一代学习技术，并增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以及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做好准备，应对涉及多个相互关联的复杂系统的所谓“棘手”问题，包括气候变化、犯罪、传染病、交通等问题。让学习者具备面对此类问题的背景是我们这个时代的主要挑战。为了全面了解此类系统，学习者需要了解系统动力学的高级方面以及管理其各个交互元素的规则。在这个项目中，协和联盟、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、用于理解复杂系统的教育技术，形成强大的混合技术。该项目与学习者一起使用它并研究其对于学习系统动力学的价值，将阐明如何促进对多个相互关联的复杂系统的深入理解。该项目将与不同学区进行研究，项目材料和技术将免费向全国研究人员和从业者提供。此外，该项目有潜力创建工具并为目前使用技术来建模和理解复杂系统作为其日常工作一部分的广大专业人士产生巨大实用性的理解。目前存在两种主要方法和技术来帮助学习推理关于复杂系统。系统动力学方法提供了系统的广泛“鹰眼视图”，有助于几乎立即了解系统及其组件内的结构和交互，而基于代理的方法提供了“蚂蚁眼视图”，揭示了系统的结构和交互。系统交互背后的细节和机制。这两种方法的交互以相似的不同粒度发生，系统动力学视图提供了快速实例化和轻松地重新构建组件之间的大规模连接的能力，而基于代理的方法提供了“精细控制旋钮”，可以对系统进行微妙的调整。系统各个参与者背后的复杂规则——在复杂的系统中，细微的调整通常会导致整个系统本身发生惊人的巨大且反直觉的变化。如果这些解释层次之间没有明确的联系，学习者的经验和理解就会支离破碎。该项目将麻省理工学院 StarLogo 基于代理的建模功能与 Concord Consortium 的 SageModeler 软件的系统建模和图表功能相结合，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 科学教室中对其进行测试。这种新技术类型首次能够让学习者在详细的个体模型和存量和流量的全局视图之间转换，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力学的推理水平。目前不可能。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功能如何导致高效的复杂系统推理，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促进强大的学习奠定基础。