Human-computer collaborative learning in citizen science

公民科学中的人机协作学习

• 批准号: EP/S027513/1
• 负责人: Advaith Siddharthan
• 金额: $ 64.72万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS027513%2F1
• 关键词: Human; computer; collaborative; learning; citizen

This project explores the potential for collaborative learning between humans and machines within the framework of environmental citizen science. The term `citizen science' encompasses public participation in science and scientific communication to the public. Although not new, citizen science has gained renewed attention because of the opportunities arising from citizens' access to digital technologies in terms of data collection and annotation. While the vast majority of citizen science projects are aimed at data gathering, we instead propose a transformational shift to a new citizen science in which the public and technology are regarded not just as sensors or data recorders, but as a collective and empowered human--artificial intelligence that can help each other in science learning.We will focus on the task of species identification from images. Citizen science projects such as iSpot invite the public to submit photos of wildlife. These are identified to species level and verified before being contributed to science. We will explore artificial intelligence as a means to automatically identify species in images. While this can save human effort, we are concerned about impact this might have on nature lovers. The introduction of technology is often associated with concerns of de-skilling. For naturalists, the honing of species identification skills is a key motivator of the recording activity. Hence, designing technology that provides opportunities for learning for both citizens and machines is essential, as is co-creating the technology to ensure that it is not only user friendly but responds to their motivations. Our approach will involve citizens collaborating with AI to arrive at a species identification. AI will narrow down the choices and inform the citizen about how to distinguish the options. The citizen in turn will through providing an identification help the machine in its learning. We will study this learning interplay with respect to collaborative species identification, but will also explore technologies that foster wider science learning, environmental consciousness and data literacy through better communication of complex citizen science data. For this we will develop technology for Natural Language Generation that can communicate complex data through language. Our proposed work programme seeks to bring about quantifiable benefits to (a) science, e.g., through the production of new knowledge and through monitoring key scientific processes at challenging temporal-spatial scales; (b) diverse stakeholders including the citizens themselves, e.g., through meaningful science learning for sustainability in formal and informal education contexts; and (c) wider society, e.g., through better societal understanding of current sustainability issues, leading to individual and societal action in support of the environment.

该项目探讨了在环境公民科学框架内人类和机器之间协作学习的潜力。"公民科学"一词包括公众参与科学和向公众传播科学。公民科学虽然不是新事物，但由于公民在数据收集和注释方面获得数字技术所带来的机会，公民科学再次受到关注。虽然绝大多数公民科学项目的目标是收集数据，但我们提出了一个向新公民科学的转型，在这种科学中，公众和技术不仅被视为传感器或数据记录器，而且被视为集体和授权的人类-人工智能，可以在科学学习中相互帮助。iSpot等公民科学项目邀请公众提交野生动物的照片。这些都是在物种水平上识别和验证的，然后才能贡献给科学。我们将探索人工智能作为自动识别图像中物种的一种手段。虽然这可以节省人力，但我们担心这可能会对自然爱好者产生影响。技术的引进往往与降低技能有关。对于博物学家来说，物种识别技能的磨练是记录活动的关键动力。因此，设计为公民和机器提供学习机会的技术至关重要，共同创造技术以确保它不仅对用户友好，而且能响应他们的动机。我们的方法将涉及公民与人工智能合作，以实现物种识别。人工智能将缩小选择范围，并告知公民如何区分这些选项。反过来，公民将通过提供身份证明来帮助机器学习。我们将研究这种学习相互作用方面的协作物种识别，但也将探索通过更好地沟通复杂的公民科学数据，促进更广泛的科学学习，环境意识和数据素养的技术。为此，我们将开发自然语言生成技术，可以通过语言交流复杂的数据。我们拟议的工作方案力求给（a）科学带来可量化的好处，例如，通过产生新知识和通过在具有挑战性的时空尺度上监测关键科学进程;（B）包括公民本身在内的各种利益攸关方，例如，通过有意义的科学学习，在正规和非正规教育背景下促进可持续性;（c）更广泛的社会，例如，通过提高社会对当前可持续性问题的认识，促使个人和社会采取行动支持环境。

项目成果

Citizen science data reveals the need for keeping garden plant recommendations up-to-date to help pollinators.

• DOI: 10.1038/s41598-020-77537-6
• 发表时间: 2020-11-24
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Anderson HB;Robinson A;Siddharthan A;Sharma N;Bostock H;Salisbury A;Roberts S;van der Wal R
• 通讯作者: van der Wal R

STEM Education Hub - Science Education in Schools: connections between Brazil and the United Kingdom

STEM 教育中心 - 学校科学教育：巴西和英国之间的联系

• 发表时间: 2022
• 作者: Ansine, J.,

X-Polli:Nation: Contributing Towards Sustainable Development Goals Through School-Based Pollinator Citizen Science

X-Polli:Nation：通过学校授粉者公民科学为可持续发展目标做出贡献

• DOI: 10.5334/cstp.567
• 发表时间: 2023
• 期刊: Theory and Practice
• 作者: Lakeman Fraser P

Consensus Building in On-Line Citizen Science

在线公民科学建立共识

• DOI: 10.1145/3555535
• 发表时间: 2022
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Sharma N