Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty
Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty
复制标题
在预测培训中嵌入沟通概念可以增加学生对生态不确定性的理解
DOI：
10.1002/ecs2.4628
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发表时间：
2023
期刊：
影响因子：
2.7
通讯作者：
Carey, Cayelan C.
中科院分区：
文献类型：
--
作者：
Woelmer, Whitney M.;Moore, Tadhg N.;Lofton, Mary E.;Thomas, R. Quinn;Carey, Cayelan C.
交流和解释生态模型预测中的不确定性是出了名的具有挑战性，这激发了对新教育工具的需求，这些工具向生态学学生介绍不确定性交流中的核心概念。生态预测是一种新兴的评估具有不确定性的生态系统未来状态的方法，它为本科生介绍不确定性交流提供了一个相关的和引人入胜的框架，因为预测可以用作解决现实世界生态问题的决策支持工具，并且具有内在的不确定性。为了提供关于不确定性沟通的关键培训，并向本科生介绍如何使用生态预测来指导决策，我们在宏观系统环境数据驱动的调查和探索(EDDIE；Macrosystems sEDDIE.org)教育项目中开发了一个动手教学模块。我们的模块使用了一种主动学习方法，将预测活动嵌入到R闪亮的应用程序中，让生态学学生参与到数据科学、生态建模和预测概念的入门课程中，而不需要高级计算或编程技能。来自生态、淡水生态学和动物学课程的250多名本科生模块前和模块后的评估数据表明，该模块显著提高了学生解释不确定性预测可视化的能力，识别不同用户交流预测不确定性的不同方式，并正确定义生态预测术语。具体地说，在模块完成后，学生更有可能描述不确定交流的视觉、数字和概率方法。学生还能够在完成模块后确定生态预测的更多好处，其中最常描述的是使用预测进行预测和决策的主要好处。这些结果表明，通过基于软件的学习，将生态模型不确定性、数据可视化和预测引入本科生态学课程是有希望的，这可以提高学生参与和理解复杂生态概念的能力。
Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE; MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts.
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