reVISit: Looking Under the Hood of Interactive Visualization Studies

reVISit: Looking Under the Hood of Interactive Visualization Studies
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reVISit:深入探究交互式可视化研究

DOI:
10.1145/3411764.3445382
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发表时间:
2021
期刊:
SIGCHI Conference on Human Factors in Computing Systems (CHI
影响因子:
--
通讯作者:
Lex, Alexander
Lex, Alexander
中科院分区:
--
文献类型:
--
作者:
Nobre, Carolina;Wootton, Dylan;Cutler, Zach;Harrison, Lane;Pfister, Hanspeter;Lex, Alexander

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当研究人员评估复杂的交互式可视化工具时,用时间和准确性等指标量化用户性能并不能显示全貌。在这样的系统中,性能通常受到统计分析方法无法解释的不同分析策略的影响。为了弥补这种缺乏细微差别,我们提出了一种新的分析方法来评估复杂的交互式可视化的规模。我们在reVISit中实现了我们的分析方法,使分析师能够在用户分析策略的背景下探索参与者交互性能指标和响应。参与者会话的重放可以帮助在试点研究期间识别可用性问题,并使个人分析过程变得突出。为了证明reVISit可视化研究的适用性,我们分析了来自两项已发表的众包研究的参与者数据。我们的研究结果表明,reVISit可以用来揭示和描述新的交互模式,分析不同的分析策略之间的性能差异,并验证或挑战设计决策。
Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users’ analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.
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