CAREER: Data-Driven User Interface Designs for Culturally Diverse Groups

职业:针对文化多元化群体的数据驱动用户界面设计

基本信息

  • 批准号:
    1651487
  • 负责人:
  • 金额:
    $ 55.64万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-07-01 至 2023-06-30
  • 项目状态:
    已结题

项目摘要

This research will systematically investigate how to make information technology more accessible to users from varied cultural backgrounds. Research shows that user interfaces designed for undifferentiated markets are less usable, less intuitive, and less appealing than are locally designed interfaces, making users less efficient and less satisfied. The results will inform novel user interface design guidelines and help build both models and tools for interface designers and end users that support the automated adaption of web interfaces to specific cultural groups. Guidelines and tools to be produced in this project will assist businesses in making decisions about, reducing costs associated with, and increasing total revenue and net income derived from preparing products for international markets. Educational goals of this project are to (1) involve high school and undergraduate students from varied cultural and demographic backgrounds in this research, (2) create and disseminate open-source educational materials that immediately affect designers' understanding and creation of inclusive website designs, and (3) use citizen scientists to participate in and promote research results to the public. To foster information inclusiveness, this research aims to translate seminal lab-based studies on visual perception into large-scale online experiments that will be administered globally on our experiment platform, LabintheWild. The research will systematically investigate the influence of human diversity on visual perception and on user interface design. Traditional academic studies conducted with small, locally recruited convenience samples are not always generalizable given their skew toward unrepresentative test subjects. This research will vastly extend conventional study populations using a proven crowdsourced experiment platform and thereby more generalizably contribute to the knowledge domains of visual perception, cultural psychology, human-computer interaction, and adaptive user interfaces by providing: (1) scientific findings on human perception that compare people from at least 30 countries and varied demographic backgrounds (2) best practice, data-driven user interface design guidelines for these groups, (3) predictive models and tools that support the automated adaption of web interfaces to varied user backgrounds, and (4) evaluations of whether these tools successfully improve work efficiency and user satisfaction.
这项研究将系统地探讨如何使信息技术更容易为来自不同文化背景的用户所利用。研究表明,与本地设计的界面相比,为无差异市场设计的用户界面的可用性更低,直观性更差,吸引力更低,使用户效率更低,满意度更低。结果将为新颖的用户界面设计指南提供信息,并帮助为界面设计师和最终用户构建模型和工具,支持Web界面自动适应特定文化群体。本项目将制定的准则和工具将协助企业作出决定,减少与为国际市场准备产品有关的费用,增加总收入和净收入。 该项目的教育目标是(1)让来自不同文化和人口背景的高中生和大学生参与这项研究,(2)创建和传播开源教育材料,立即影响设计师对包容性网站设计的理解和创作,(3)利用公民科学家参与并向公众推广研究成果。为了促进信息的包容性,这项研究旨在将基于实验室的视觉感知研究转化为大规模的在线实验,这些实验将在我们的实验平台LabintheWild上进行全球管理。该研究将系统地调查人类多样性对视觉感知和用户界面设计的影响。传统的学术研究进行了小,当地招募的方便样本并不总是具有普遍性,因为他们倾向于不具代表性的测试对象。这项研究将使用经过验证的众包实验平台极大地扩展传统的研究人群,从而更普遍地为视觉感知,文化心理学,人机交互和自适应用户界面的知识领域做出贡献:(1)对来自至少30个国家和不同人口背景的人进行比较的关于人类感知的科学结论(2)最佳做法,这些群体的数据驱动的用户界面设计指南,(3)支持Web界面自动适应不同用户背景的预测模型和工具,以及(4)评估这些工具是否成功地提高了工作效率和用户满意度。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
How Online Tests Contribute to the Support System for People With Cognitive and Mental Disabilities
在线测试如何为认知和精神障碍人士的支持系统做出贡献
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Katharina Reinecke其他文献

Volunteer-Based Online Studies With Older Adults and People with Disabilities
针对老年人和残疾人的基于志愿者的在线研究
Accurate measurements of pointing performance from in situ observations
通过现场观测准确测量指向性能
Apéritif: Scaffolding Preregistrations to Automatically Generate Analysis Code and Methods Descriptions
Apéritif:搭建预注册以自动生成分析代码和方法描述
Respectful Language as Perceived by People with Disabilities
残疾人眼中的尊重语言
Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning
文化协调的道德机器:人工智能通过逆强化学习对人类价值系统进行内隐学习
  • DOI:
    10.48550/arxiv.2312.17479
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Nigini Oliveira;Jasmine Li;Koosha Khalvati;Rodolfo C Barragan;Katharina Reinecke;A. Meltzoff;Rajesh P. N. Rao
  • 通讯作者:
    Rajesh P. N. Rao

Katharina Reinecke的其他文献

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{{ truncateString('Katharina Reinecke', 18)}}的其他基金

Collaborative Research: IIS Core: Small: World Values of Conversational AI and the Consequences for Human-AI Interaction
协作研究:IIS 核心:小:对话式 AI 的世界价值以及人机交互的后果
  • 批准号:
    2230466
  • 财政年份:
    2023
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
Institutional Transformation: Anticipating Undesirable Consequences of Computer Science Research
机构转型:预测计算机科学研究的不良后果
  • 批准号:
    2315937
  • 财政年份:
    2023
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant
CHS: Small: Exploring and Predicting Unintended Consequences of Technology
CHS:小:探索和预测技术的意外后果
  • 批准号:
    2006104
  • 财政年份:
    2020
  • 资助金额:
    $ 55.64万
  • 项目类别:
    Standard Grant

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