课题基金 / 基金详情

Adaptation and Personalization for Information Visualization

Adaptation and Personalization for Information Visualization
信息可视化的适应和个性化
批准号:
RGPIN-2016-04611
负责人:
Conati, Cristina
金额:
$3.35万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

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中文摘要
翻译
信息可视化(InfoVis)中的*研究*传统上遵循*一刀切的方法,不考虑用户差异。尽管越来越多的证据表明,用户自适应交互,即适应每个用户的特定需求和能力的交互,有可能在与可视化交互的同时改善用户体验,但用户差异和不同形式的适应在信息可视化中的影响仍然很大程度上仍未被探索。为了填补这一空白,在过去的4年里,申请人从事了新颖的研究,为自适应可视化奠定了基础,这种可视化可以帮助定制*视觉显示,以满足特定用户的需求和能力。通过几项用户研究,我们发现,感知速度、视觉和语言工作记忆等认知能力可以影响不同视觉化的用户体验,并且它们的影响受任务复杂性的影响。这些研究还基于对通过眼球跟踪器收集的用户凝视模式的分析,找出了这些认知能力有不同衡量标准的用户表现发生变化的可能原因。例如,研究发现,感知速度低的用户在处理条形图图例上花费了更多的精力,这往往会降低他们的性能。此结果*表明这些用户可能受益于处理*图例的支持。我们还表明,凝视数据可以被用来实时预测与适应相关的用户和任务特征。这些结果提供了重要证据,表明用户自适应可视化值得研究,但它们只触及了为可视化处理提供有效个性化所需的技术的皮毛。因此,这项提议旨在*对用户自适应可视化进行进一步研究,以*关闭所谓的自适应循环。*尤其是,我们将研究如何设计和提供有效和不引人注目的个性化。我们还将研究如何检测特定的用户瞬时状态,这些状态指示用户在可视化处理期间最需要帮助的时间,例如,当用户处于混乱状态时。最后,我们将研究建议方法的实际应用,例如*为阅读复杂的多模式文档提供自适应支持(例如,*《经济学人》的文章),其中文本描述了嵌入图形的*不同方面。*正常*0*FALSE**EN-CA*X-NONE*X-NONE****/*样式定义*/*表。MsoNormal表*{mso-style-name:“表正常”;*mso-tstyle-rowband-size:0;*mso-tstyle-colband-size:0;*mso-style-noshow:yes;*mso-style-first:99;*mso-style-parly:“”;*mso-pding-alt:0in 5.4pt 0in 5.4pt;*mso-para-edge-top:0in;*mso-para-edge-right:0in;*mso-para-mark-Bottom:10.0pt;*mso-par-边距-Left:0in;*行高:115%;*mso-Page:Widow-Leaban;*FONT-SIZE:11.0pt;*FONT-FAMILY:“CALIBRI”,“sans-serif”;*mso-ascii-FONT-FAMILY:CALIBRI;*mso-ascii-heme-FONT:Minor-拉丁语;*mso-hansi-FONT-FAMILY:CARIBRI;*mso-hansi-heme-FONT:Minor-拉丁语;*mso-fareast-Language:EN-US;}**
英文摘要
* * * **Research*in Information Visualization (Infovis) has traditionally followed a*one-size-fits-all approach that does not account for user differences. Despite*increasing evidence that user-adaptive*interaction, i.e. interaction adapted to suit each user's specific needs and*abilities, has the potential to improve users' experience while interacting with visualizations, the effects of both user differences and different forms of*adaptation in information visualization remain largely unexplored. In order to*fill this gap, in the last 4 years the applicant has engaged in novel research to lay the foundations for adaptive visualization that can help tailor*visual displays to specific user needs and abilities. Through several user*studies, we showed that cognitive abilities such as perceptual speed,*and visual and verbal working memory can impact user experience with*different visualizations, and that their*effect is mediated by task complexity. These studies also identified possible*reasons for the change in performance for users with different measures of*these cognitive abilities, based on the analysis of user's gaze patterns collected via an eye-trackers. For instance, users with low perceptual speed were*found to spend more effort in processing the legend in bar graph*visualizations, which tends to slow down their performance. This result*indicates that these users might benefit from support in processing *legends. We also showed that gaze data can be leveraged to predict, in real-time, user and task characteristics relevant for adaptation. These results*provided important evidence that user-adaptive visualizations are worth*investigating, but they only scratch the surface of the know-how needed to*deliver effective personalization for visualization processing. Thus, this proposal aims to*conduct further research on user-adaptive visualizations that will allow*closing the so called adaptive loop.*In particular, we will investigate how to design and deliver personalization that is effective and unobtrusive. We*will also research how to detect specific user transient states that indicate*when the user is most in need of help during visualization processing, e.g.*when the user is in a state of confusion. Finally, we will investigate practical applications of the proposed approaches, for instance*providing adaptive support for reading complex multimodal documents (e.g.,*articles from the Economist) where text describes*different aspects of the embedded graphs.**** * Normal* 0* * * * * * * * * false* false* false* * EN-CA* X-NONE* X-NONE* * * * * * * * * * * * * * * * * * * * * * * * ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *** /* Style Definitions */* table.MsoNormalTable* {mso-style-name:"Table Normal";* mso-tstyle-rowband-size:0;* mso-tstyle-colband-size:0;* mso-style-noshow:yes;* mso-style-priority:99;* mso-style-parent:"";* mso-padding-alt:0in 5.4pt 0in 5.4pt;* mso-para-margin-top:0in;* mso-para-margin-right:0in;* mso-para-margin-bottom:10.0pt;* mso-para-margin-left:0in;* line-height:115%;* mso-pagination:widow-orphan;* font-size:11.0pt;* font-family:"Calibri","sans-serif";* mso-ascii-font-family:Calibri;* mso-ascii-theme-font:minor-latin;* mso-hansi-font-family:Calibri;* mso-hansi-theme-font:minor-latin;* mso-fareast-language:EN-US;}**
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Toward Personalized Explainable AI
  • 批准号:
    RGPIN-2022-03727
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.5万
  • 财政年份:
    2022
  • 负责人:
    Conati, Cristina
  • 依托单位:
AI-Driven personalized support to foster computational thinking skills in early K12 education
  • 批准号:
    567500-2021
  • 项目类别:
    Alliance Grants
  • 资助金额:
    $7.33万
  • 财政年份:
    2021
  • 负责人:
    Conati, Cristina
  • 依托单位:
Adaptation and Personalization for Information Visualization
  • 批准号:
    RGPIN-2016-04611
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.35万
  • 财政年份:
    2021
  • 负责人:
    Conati, Cristina
  • 依托单位:
Adaptation and Personalization for Information Visualization
  • 批准号:
    RGPIN-2016-04611
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.35万
  • 财政年份:
    2020
  • 负责人:
    Conati, Cristina
  • 依托单位:
海外基金