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

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，涉及来自密歇根州立大学（奖项 DUE-1711260）、美国自然历史博物馆（奖项 DUE-1711411）和罗格斯大学新不伦瑞克分校（奖项 DUE-1712034）的研究人员。了解大规模生态系统变化、粮食安全和全球渔业衰退等复杂的跨学科问题需要下一代科学家发展系统思维（ST）技能，使他们能够了解人类与自然耦合的结构和功能系统并确定最有效和高效地解决问题的杠杆点。能够深入思考系统复杂动态的学生可以更好地预测系统的行为，设计更有利的结果，并评估不同政策和管理决策之间的权衡。然而，尽管科学技术技能通常是全国大学可持续科学课程的基础，但教师可用于在课堂上推广和评估科学技术技能的资源却很少。在这个项目中，研究人员将测试一种在 STEM 课堂上教学、评估和提高 ST 技能的方法，使用名为 Mental Modeler (http://www.mentalmodeler.org/) 的认知绘图软件，该软件允许学生表达他们对通过易于使用的系统建模工具来分析问题并重新排列问题的组成部分以分析场景并修改他们的理解。这种方法将在几个环境科学、环境研究和可持续发展相关课程中进行测试，重点关注与粮食系统相关的问题。在之前 NSF 资助的研究的基础上，该项目将 (1) 生产支持和衡量 ST 学习的工具和模块综合 STEM 学科；(2) 进一步开发和测试用于可视化复杂系统的 Mental Modeler 软件。 Mental Modeler 是一个多维可视化和建模软件平台，允许用户识别系统的结构、功能和杠杆点，并运行系统级场景分析。该软件将帮助教育工作者确定适用于 STEM 领域的 ST 评估的各个方面，并衡量学生随时间的 ST 学习情况。具体来说，该项目将解决三个问题：（1）适用于 STEM 领域的标准化 ST 评估维度有哪些？ (2) 学生的 ST 学习进展是否可以通过结构网络指标、功能场景分析和半定量认知图（a）特定课程内和（b）课程中跨课程定义的定性描述来衡量随着时间的推移？ (3) ST 方面是否存在可预测的学生学习轨迹或进展？