CAREER: Promoting Metacognition in Visual Analytics
CAREER: Promoting Metacognition in Visual Analytics
批准号:
2340539
负责人:
Emily Wall
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
项目状态:
未结题
起止时间:
2024-06-01 至 2029-05-31
中文摘要
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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis
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批准号:2311574
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项目类别:Standard Grant
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资助金额:$68.31万
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财政年份:2023
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负责人:Emily Wall
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依托单位:
海外基金