Collaborative Research: Assessing "Systems Thinking" Skills and Learning in Interdisciplinary STEM Courses

协作研究：评估跨学科 STEM 课程中的“系统思维”技能和学习

• 批准号: 1711411
• 负责人: Eleanor Sterling
• 金额: $ 8.65万
• 依托单位: American Museum Natural History
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711411&HistoricalAwards=false
• 关键词: Collaborative; Research; Assessing; Systems; Thinking; Collaborative; Research; Assessing; Systems; Thinking

This is a collaborative project involving investigators from Michigan State University (Award DUE-1711260), the American Museum of Natural History (Award DUE-1711411), and Rutgers University-New Brunswick (Award DUE-1712034). Understanding complex interdisciplinary issues such as large-scale ecosystem change, food security, and the decline in global fisheries requires that the next generation of scientists develop Systems Thinking (ST) skills that will allow them to understand the structure and function of coupled human and natural systems and identify the leverage points for addressing problems most effectively and efficiently. Students who are able to think deeply about the complex dynamics of a system are better prepared to predict the system's behavior, engineer more favorable outcomes, and evaluate the trade-offs between different policy and management decisions. However, even though ST skills are often the foundation of sustainability science curricula at universities nationwide, few resources are available for instructors to promote and assess ST skills in their classrooms. In this project, the investigators will test a method for teaching, assessing, and improving ST skills in STEM classrooms using cognitive mapping software called Mental Modeler (http://www.mentalmodeler.org/), which allows students to represent their understanding of a problem through easy-to-use system-modeling tools and to rearrange components of the problem to analyze scenarios and revise their understanding. This approach will be tested in several environmental science, environmental studies, and sustainability-related courses focusing on problems related to food systems.Building on previous NSF-funded research, this project will (1) produce tools and modules that support and measure ST learning in integrated STEM disciplines and (2) further develop and test the Mental Modeler software for visualizing complex systems. Mental Modeler is a multidimensional visualization and modeling software platform that allows users to identify the structure, function, and leverage points of a system and to run system-level scenario analyses. The software will help educators determine aspects of ST assessment that are applicable across STEM fields and measure students' ST learning over time. Specifically, the project will address three questions: (1) What are the standardized ST assessment dimensions that are applicable across STEM fields? (2) Can students' ST learning progressions be measured over time through structural network metrics, functional scenario analyses, and qualitative descriptions defined within semi-quantitative cognitive maps (a) within a specific course and (b) across courses in a curriculum? (3) Are there predictable student learning trajectories or progressions with regard to ST?

这是一个合作项目，涉及密歇根州立大学的调查员（奖励1711260奖），美国自然历史博物馆（奖励1711411奖）和罗格斯大学 - 新不伦瑞克省（Dudure-1712034奖）。了解复杂的跨学科问题，例如大规模的生态系统变化，粮食安全以及全球渔业的下降，要求下一代科学家发展系统思维（ST）技能，使他们能够使人类和自然系统的结构和功能了解人类和自然系统的结构和功能，并确定最有效和有效地解决问题的杠杆点。能够对系统的复杂动态进行深入思考的学生已经准备好预测系统的行为，工程师更有利的结果，并评估不同政策和管理决策之间的权衡。但是，即使ST技能通常是全国大学可持续发展科学课程的基础，但教师几乎没有资源来促进和评估教室中的ST技能。在该项目中，研究人员将使用名为Mental Model Modeler（http://www.mentalmodeler.org/）的认知映射软件（http://www.mentalmodeler.org/）来测试一种教学，评估和提高ST教室中的ST技能的方法，该软件使学生能够通过易于使用的系统建模工具和重新分析问题的组件来代表他们对问题的理解，并将其重新置于问题的情况下，并将其重新分析和改进其理解和对其进行改进。该方法将在几种环境科学，环境研究和与可持续性有关的课程中进行测试，这些课程的重点是与食品系统有关的问题。建立对先前NSF资助的研究的建设，该项目将（1）生产工具和模块，以支持和测量综合的STEM学科中的ST学习以及（2）进一步开发和测试用于可视化和测试可视化组件的复杂系统。心理建模者是一个多维可视化和建模软件平台，可允许用户识别系统的结构，功能和利用点并运行系统级别的方案分析。该软件将帮助教育者确定ST评估的各个方面，这些方面适用于STEM领域，并随着时间的推移来衡量学生的ST学习。具体来说，该项目将解决三个问题：（1）在STEM领域中适用的标准化ST评估维度是什么？ （2）是否可以通过结构网络指标，功能场景分析以及在特定课程中的半定量认知图（a）中定义的定性描述来测量学生的ST学习进度，并在课程中跨课程中定义的定性描述？ （3）关于ST的学生学习轨迹或进步是否可预测？