CAREER: Promoting Metacognition in Visual Analytics
职业:促进视觉分析中的元认知
基本信息
- 批准号:2340539
- 负责人:
- 金额:$ 64.4万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-06-01 至 2029-05-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Data-driven decision-making requires the people engaged in it to make a number of choices about the data to collect, and the methods for collecting it and analyzing it, as well as the interpretation of the results. Cognitive, cultural, and data biases can interfere with these processes at every stage, requiring data analysts to be thoughtful and reflective as they do their work. This project’s goal is to help analysts reduce their biases through tools that help them critically assess their thought processes using metacognition, or thinking about thinking. Metacognition will be a guiding idea for developing tool features that help analysts be aware of possible biases. Studies of metacognition have shown that it can be helpful in other educational and analysis settings; this project will use ideas from those studies to develop methods that identify potential biases and present activities to help analysts avoid them. The project team will also create educational materials and work with non-profit partner organizations to help the general public think more deeply about their own analytic strategies and how they might be improved. This project will apply theories of metacognition to address human biases and improve decision-making processes on an individual level. The project is structured around four research thrusts. The researchers will first organize theories of metacognition and translate them into an actionable design space of metacognitive interventions in visual analytics (Thrust I). Next, the researchers will work alongside non-profit partner organizations to co-design and develop a suite of metacognitive interventions (Thrust II) and evaluate those interventions in a series of laboratory experiments (Thrust III). Finally, the researchers will assess the extent to which empirical findings translate to real-world efficacy in a case study deployment of metacognitive interventions (Thrust IV). This work will test whether meta-cognitive interventions, when successfully applied in data-driven decision-making, can both boost awareness of the technical accuracy of analytic results and foster more thoughtful, socially accountable, and diligent practices in data analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
数据驱动的决策要求参与决策的人对要收集的数据、收集和分析数据的方法以及对结果的解释做出许多选择。认知、文化和数据偏见会在每个阶段干扰这些过程,这就要求数据分析师在工作时深思熟虑。这个项目的目标是帮助分析师减少他们的偏见,通过工具,帮助他们批判性地评估他们的思维过程使用元认知,或思考思考。元认知将成为开发工具功能的指导思想,帮助分析师意识到可能的偏见。元认知的研究表明,它可以在其他教育和分析环境中有所帮助;本项目将利用这些研究的想法来开发识别潜在偏见的方法,并提出帮助分析师避免偏见的活动。该项目团队还将创建教育材料,并与非营利合作伙伴组织合作,帮助公众更深入地思考自己的分析策略以及如何改进。该项目将应用元认知理论来解决人类偏见,并改善个人层面的决策过程。该项目围绕四个研究方向展开。研究人员将首先组织元认知理论,并将其转化为视觉分析中元认知干预的可操作设计空间(推力I)。接下来,研究人员将与非营利合作伙伴组织合作,共同设计和开发一套元认知干预措施(Thrust II),并在一系列实验室实验(Thrust III)中评估这些干预措施。最后,研究人员将评估实证研究结果在多大程度上转化为现实世界的有效性,在一个案例研究部署的元认知干预措施(推力四)。这项工作将测试元认知干预,当成功地应用于数据驱动的决策,既可以提高对分析结果的技术准确性的认识,并促进更周到,社会责任,和勤奋的数据分析实践。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Emily Wall其他文献
Peer-Mediated Interventions to Enhance Conversation Skills of Young Adults with Intellectual and Developmental Disabilities on a University Campus
- DOI:
10.1007/s10882-024-09979-z - 发表时间:
2024-06-19 - 期刊:
- 影响因子:1.500
- 作者:
Leslie Ann Bross;Emily Wall;Monique Pinczynski;Ashley Anderson;Thai Williams;Charles L. Wood;Fred Spooner - 通讯作者:
Fred Spooner
Supporting Team-First Visual Analytics through Group Activity Representations
通过小组活动表示支持团队优先的可视化分析
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Sriram Karthik Badam;Zehua Zeng;Emily Wall;A. Endert;N. Elmqvist - 通讯作者:
N. Elmqvist
Let’s Get Vysical: Perceptual Accuracy in Visual & Tactile Encodings
让我们来了解一下 Vysical:视觉和触觉编码中的感知准确性
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Zhongzhen Xu;Kristin Williams;Emily Wall - 通讯作者:
Emily Wall
Human-Centered Approaches for Provenance in Automated Data Science
自动化数据科学中以人为本的起源方法
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
∗. AnamariaCrisan;∗. LarsKotthoff;∗. MarcStreit;∗. KaiXu;A. Endert;Alexander Lex;Alvitta Ottley;C. Brumar;L. Battle;Mennatallah El;.. NadiaBoukhelifa.....;Jen Rogers;Emily Wall;Mehdi Chakhchoukh;Marie Anastacio;Rebecca Faust;C. Turkay;Steffen Koch;A. Kerren;Jürgen Bernard;Klaus Eckelt;Sheeba Samuel;David Koop;Kiran Gadhave;Dominik Moritz;Lars Kotthof;T. Tornede;C. Walchshofer;A. Hinterreiter;Holger Stitz;Marc Streit Main - 通讯作者:
Marc Streit Main
Using Expert Patterns in Assisted Interactive Machine Learning: A Study in Machine Teaching
在辅助交互式机器学习中使用专家模式:机器教学研究
- DOI:
- 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Emily Wall;S. Ghorashi;Gonzalo A. Ramos - 通讯作者:
Gonzalo A. Ramos
Emily Wall的其他文献
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{{ truncateString('Emily Wall', 18)}}的其他基金
Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis
合作研究:HCC:媒介:可视化数据分析中的建模和减轻确认偏差
- 批准号:
2311574 - 财政年份:2023
- 资助金额:
$ 64.4万 - 项目类别:
Standard Grant
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