MSB-ECA: A macrosystems science training program: developing undergraduates' simulation modeling, distributed computing, and collaborative skills

生态学家越来越多地使用计算机模型，包括通过环境传感器网络获得的广泛观察，来研究湖泊和森林并预测未来的变化。进行这种建模，以及理解模型结果，需要数据分析、定量推理和计算方面的技能。然而，建模和计算技能很少在本科课堂上教授，这代表了培养学生应对复杂环境挑战的主要差距。该项目将开发一个培训项目，通过模拟建模向全美数千名学生传授宏观系统生态学的基础知识。通过该项目，教师和学生将学习如何利用计算机科学中出现的新资源快速有效地运行模型。因此，学生将同时学习宏观系统科学的核心概念，并培养进行下一代环境研究所需的技能。这些概念和技能将嵌入以宏观系统生态学前沿为中心的教学模块中，使本科生能够为高层次的宏观系统研究做出贡献。让本科生参与实际应用的动手建模活动，转化为具有更高数据科学、系统思维和定量技能的劳动力。这个项目将创建一个培训项目（MacroSystems EDDIE：环境数据驱动的调查和探索），将本科生与新的计算工具联系起来，通过模拟建模来教授他们宏观系统科学的基础知识。这些工具将嵌入到独立的教学模块中，这些模块将广泛适用于一系列学科和本科生的经验水平。拟议的模块代表了本科生的第一个正式的宏观系统科学课程，将通过与科学教育研究中心的合作，严格评估、修订和广泛传播，以产生最大的影响。这些模块将围绕积极的研究探究领域进行设计，例如，对湖泊温度的相互作用和区域影响，碳循环，藻华的发生率，大陆尺度的水政策，学生将被鼓励在宏观系统科学的前沿提出新的问题，他们将使用模拟模型来回答。作为培训计划的一部分，学生们将与全球湖泊生态观测站网络（GLEON）的科学工作组分享他们的成果，以推进正在进行的宏观系统研究。拟议的项目代表了向本科生教授宏观系统生物学的创新方法，并有可能推动美国本科STEM教育。在该项目期间，至少有750名学生将参加macrosystems EDDIE模块，通过科学教育研究中心的传播，有可能增加数千名学生。学生参与Macrosystems EDDIE模块将产生科学发现，这将促进我们对宏观系统生物学的理解

Ecologists are increasingly using computer models, involving extensive observations obtained through environmental sensor networks, to study lakes and forests and predict future change. Conducting this modeling, as well as understanding the model results, requires skills in data analysis, quantitative reasoning, and computing. However, modeling and computational skills are rarely taught in undergraduate classrooms, representing a major gap in training students to tackle complex environmental challenges. This project will develop a training program that teaches thousands of students across the U.S. the foundations of macrosystems ecology through simulation modeling. Through the program, instructors and their students will learn how to quickly and efficiently run the models by using new resources emerging from computer science. Thus, students will simultaneously learn the core concepts of macrosystems science and develop the skills needed to conduct the next generation of environmental research. These concepts and skills will be embedded within teaching modules centered on the frontier of macrosystems ecology, enabling undergraduate students to contribute to high-level macrosystems research. Engaging undergraduate students in hands-on modeling activities with real-world applications translates into a workforce with increased data science, systems thinking, and quantitative skills.This project will create a training program (MacroSystems EDDIE: Environmental Data-Driven Inquiry & Exploration) that connects undergraduate students with new computing tools to teach them the foundations of macrosystems science using simulation modeling. These tools will be embedded in stand-alone teaching modules that will be widely applicable to a suite of disciplines and undergraduate student experience levels. The proposed modules represent the first formalized macrosystems science curriculum for undergraduates, which will be rigorously assessed, revised, and disseminated broadly for maximum impact through a partnership with the Science Education Research Center. The modules will be designed around areas of active research inquiry e.g., interacting local and regional effects on lake temperatures, carbon cycling, the incidence of algal blooms, continental-scale water policy and students will be encouraged to develop novel questions at the frontier of macrosystems science that they will answer using simulation models. As part of the training program, the students will share their results with Global Lake Ecological Observatory Network (GLEON) scientific working groups to advance ongoing macrosystems research. The proposed program represents an innovative approach for teaching macrosystems biology to undergraduates and has the potential to advance undergraduate STEM education across the U.S. A minimum of 750 students will participate in the MacroSystems EDDIE modules during this project, with the potential for several thousands more through dissemination by Science Education Research Center. student participation in the Macrosystems EDDIE modules will generate scientific discoveries that will advance our understanding of macrosystems bio