Titlebook: Adaptive and Personalized Visualization; Alvitta Ottley Book 2020 Springer Nature Switzerland AG 2020

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书目名称Adaptive and Personalized Visualization被引频次学科排名




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书目名称Adaptive and Personalized Visualization年度引用学科排名




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SPURN

https://doi.org/10.1007/3-540-32786-Xrt rate, eye gaze, brain imaging, etc.) for evaluating visualizations. At the same BELIV workshop, Ji Soo Yi [176] proposed studying individual differences when evaluating visualizations. Yi argued that understanding how users differ in personality and cognitive factors is important in evaluating vi

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https://doi.org/10.1007/3-540-32786-Xtes express our situational condition at a specific time. Cognitive traits reveal stable factors that distinguish one person from another, and is arguably to most important of the three dimensions. As such, numerous studies have focused on identifying cognitive traits or “stable tendencies to respon

bourgeois

https://doi.org/10.1007/3-540-32786-X representation can make traditionally difficult problems more concrete and easier to understand. In practice, however, nuanced contextual and individual factors heavily impact the effectiveness of visualizations, making the right representation difficult to establish.

SPER

https://doi.org/10.1007/3-540-32786-Xr’s spatial ability is a mediating factor, and that we can use spatial ability to predict performance across different representations of a Bayesian reasoning problem. Furthermore, we saw that none of the tested conditions were ideal for all users, highlighting the importance of tailoring designs to

overrule

Basic Methods for the Biochemical Labtion would hinge on the assumption that LOC not only impacts speed and accuracy, but it also influences . a user interacts with an interface. In this chapter, we explore the feasibility of personalized visualizations by analyzing users’ interactions to investigate the relationship between LOC, visua