Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety
Asynchronous student engagement in analysis of climate data achieves learning objectives related to climate change understanding, statistical competence, and climate anxiety
复制标题
学生异步参与气候数据分析，实现与气候变化理解、统计能力和气候焦虑相关的学习目标
DOI：
10.1080/10899995.2023.2193810
复制
发表时间：
2023
影响因子：
--
通讯作者：
Darner, R.
中科院分区：
文献类型：
--
作者：
Meixner, T.;Ciancarelli, B.;Farrell, E. P.;García, D. Silva;Josek, T.;Kelly, M. M.;Meister, P.;Soule, D.;Darner, R.
在过去几十年中，在异步在线环境中学习变得越来越重要，而COVID-19大流行带来的教育环境变化似乎增加了这一需求。科学教育工作者和学生需要了解哪些方法在异步环境中有效，与其他教学模式相比，非正式反馈往往减少。在这项研究中，我们异步实施了一个学习模块，在5个机构，引导学生（N = 199）从规定的数据分析，通过引导查询，并最终开放查询。该模块侧重于气候变化背后的科学。学生使用专业科学家使用的相同真实数据集来研究地质历史和气候变化的原因。通过分析当代大气二氧化碳和温度数据，然后使用80万年的记录从东方冰芯大气属性代理记录，学生确定气候变化的原因，并发现最近的大气变化的前所未有的性质。使用前/后模块评估，我们证明了学生对气候变化过程和用于分析数据的统计方法的理解的提高。然而，没有证据表明该模块发展了学生对因果关系和相关性之间关系的科学推理。学生们坚持认为，相关性不是因果关系，即使有一个强大的因果机制（即，温室效应）解释了大气中二氧化碳和温度之间的联系。最后，我们的分析表明，一般来说，对气候变化的焦虑在模块中减少，这样的学生变得不太担心气候变化的越多，他们了解它。然而，科学否认的学生经历了更高的焦虑比学生谁接受气候变化的科学共识。在这项研究中，对气候科学持异议的学生很少，以至于不可能进行统计比较，但这一有趣的发现值得进一步研究焦虑在科学否认中的作用。主要是，这项研究表明，异步在线学习环境确实可以支持实现与进行真实科学相关的学习目标，例如增加对气候变化和统计概念的理解，同时不会引发对气候变化的焦虑。
Learning in asynchronous online environments has gained importance over the last several decades, and educational environment shifts from the COVID-19 pandemic appear to have increased this need. Science educators and students need information about which approaches work in the asynchronous environment where informal feedback tends to be reduced, compared to other teaching modalities. In this study, we asynchronously implemented a learning module across 5 institutions that guided students (N = 199) from prescriptive data analysis through guided inquiry and eventually to open inquiry. The module focuses on the science behind climate change. Students work with the same authentic data sets used by professional scientists to examine geologic history and causes of climate change. By analyzing contemporary atmospheric carbon dioxide and temperature data and then using the 800,000-year record available from the Vostok ice core proxy record of atmospheric properties, students identify the causes of climate change and discover the unprecedented nature of recent atmospheric changes. Using a pre/post-module assessment, we demonstrate improvement in students’ understanding of climate change processes and statistical methods used to analyze data. However, there was no evidence that the module develops students’ scientific reasoning about the relationship between causation and correlation. Students maintained that correlation is not causation, even when a robust causal mechanism (i.e., the greenhouse effect) explains the link between atmospheric carbon dioxide and temperature. Finally, our analysis indicated that generally, anxiety about climate change was reduced during the module, such that students become less anxious about the climate change the more they learn about it. However, science-denying students experienced much higher anxiety about climate change than students who accepted the scientific consensus about climate change. Climate science-dissenting students were so few in this study that a statistical comparison was not possible, but this intriguing finding warrants further investigation of the role of anxiety in science denial. Mainly, this study demonstrates how asynchronous online learning environments can indeed support the achievement of learning objectives related to conducting authentic science, such as increasing understanding of climate change and statistical concepts, all while not provoking anxiety about climate change.
登录
查看更多内容
皮尔士哲学中的科学发现方法：演绎、归纳、溯因
影响因子：
0.8
作者：
Cassiano Terra Rodrigues
通讯作者：
Cassiano Terra Rodrigues
EDDIE 模块是培养定量素养和地震学理解的有效学习工具
影响因子：
--
作者：
D. Soule;R. Darner;Catherine M. O'Reilly;N. Bader;T. Meixner;C. A. Gibson;R. E. McDuff
通讯作者：
R. E. McDuff
解释我们为什么喜欢或讨厌统计的心理模型
DOI：
10.1016/j.kjss.2016.08.013
发表时间：
2017
期刊：
Cognition in Mathematics
影响因子：
--
作者：
Tommy Prayoga;Juneman Abraham
通讯作者：
Juneman Abraham
利用丰富的背景和数据探索来提高对气候数据和数据素养的参与：将现场站带入大学课堂
影响因子：
--
作者：
A. Ellwein;L. Hartley;S. Donovan;I. Billick
通讯作者：
I. Billick
青年人对气候变化的声音：利用因素分析来理解科学、政治和情感的交叉点
DOI：
--
发表时间：
2020
期刊：
影响因子：
--
作者：
L. Zummo;Emma C. Gargroetzi;Antero Garcia
通讯作者：
Antero Garcia