Consensus Building in On-Line Citizen Science
Consensus Building in On-Line Citizen Science
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在线公民科学建立共识
DOI：
10.1145/3555535
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发表时间：
2022
影响因子：
--
通讯作者：
Sharma N
中科院分区：
文献类型：
--
作者：
Sharma N
许多倡议邀请公众成员执行在线分类任务，例如识别图像中的物体。这些任务对许多不同学科的大型公民科学项目至关重要，志愿者利用他们的知识和在线支持工具，例如，识别野生动物的种类或根据它们的形状对星系进行分类。然而，对于复杂的分类任务，比如这个识别大黄蜂物种的案例研究，在志愿者之间——甚至在专家之间——达成一致可能需要建立共识的过程。协作和团队解决问题和决策的方法已被广泛记录，以改善现实世界中的任务性能和用户学习。大多数这些过程和项目都是通过异步方式提供的在线反馈来调解的，因此本文解决了一个中心研究问题：参与物种识别任务的参与者如何响应在线协作中提供的不同形式的反馈，旨在支持同伴学习并提高任务绩效？我们在一个协作任务中测试了四种不同的反馈方法，在这个任务中，参与者根据一个长期运行的在线公民科学计划从他们的同行那里收集的信息，审查了他们以前注释过的数据。所选的介面在社会科学和心理学文献中有很强的基础，可以应用于公民科学实践以及其他在线社区。结果表明，虽然所有四种方法都提高了准确性，但基于合作之前存在的共识类型，存在差异。这些差异突出了协作期间不同形式的反馈对于提高识别数据准确性和进一步提高用户在识别任务方面的专业知识的有用性。我们发现，在社会学习界面上发布的匿名和目标导向的免费文本评论在提高数据准确性和为同行学习创造机会方面最有效，特别是在物种识别任务更困难的情况下。本研究对于将公民科学的实践扩展到正式和非正式的学习环境以及接触到各种用户具有重要意义。
A number of initiatives invite members of the public to perform online classification tasks such as identifying objects in images. These tasks are crucial to numerous large-scale Citizen Science projects in different disciplines, with volunteers using their knowledge and online support tools to, for example, identify species of wildlife or classify galaxies by their shapes. However, for complex classification tasks, such as this case study on identifying species of bumblebee, reaching an agreement between volunteers - or even between experts~-~may require consensus-building processes. Collaboration and teamwork approaches to problem solving and decision-making have been widely documented to improve both task performance and user learning in the real world. Most of these processes and projects are mediated online through feedback delivered in an asynchronous manner, and this article thus addresses a central research question: How do participants involved in species identification tasks respond to different forms of feedback provided in online collaboration, designed to support peer-learning and improve task performance? We tested four different approaches to feedback within a collaboration task, where participants reviewed their previously annotated data based on information curated from their peers on a long running online citizen science initiative. The selected interfaces have a strong foundation in social science and psychology literature and can be applied to citizen science practices as well as other online communities. Results showed that while all four approaches increased accuracy, there were differences based on the types of consensus that existed before collaboration. Such differences highlight the usefulness of different forms of feedback during collaboration for increasing data accuracy of identification and furthering users' expertise on identification tasks. We found that anonymised and goal-directed free text comments posted on social learning interfaces were most effective in improving data accuracy as well as creating opportunities for peer-learning, particularly where the species identification task was more difficult. This study has significant implications for extending the practice of citizen science across formal and informal learning environments and reaching out to a variety of users.
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DOI：
10.1609/icwsm.v8i1.14539
发表时间：
2014
期刊：
Proceedings of the International AAAI Conference on Web and Social Media
影响因子：
--
作者：
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作者：
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L. Trouille
帮助或阻碍：公民科学中的实时聊天
影响因子：
3
作者：
Ramine Tinati;E. Simperl;Markus Luczak
通讯作者：
Markus Luczak
DOI：
10.1016/j.ijhcs.2021.102605
发表时间：
2021-02-06
影响因子：
5.4
作者：
Palacin, Victoria;Ferrario, Maria Angela;Porras, Jari
通讯作者：
Porras, Jari
通过在线公众参与加速生物多样性研究标本数字化
影响因子：
--
作者：
Elizabeth R. Ellwood;B. A. Dunckel;P. Flemons;R. Guralnick;Gil Nelson;Greg Newman;Sarah Newman;Deborah L Paul;G. Riccardi;N. Rios;Katja C. Seltmann;Austin R Mast
通讯作者：
Austin R Mast