Towards Adaptive Information Visualization: On the Influence of User Characteristics

Abstract

Abstract. The long-term goal of our research is to design information visualization systems that adapt to the specific needs, characteristics, and context of each individual viewer. In order to successfully perform such adaptation, it is crucial to first identify characteristics that influence an individual user's effectiveness, efficiency, and satisfaction with a particular information visualization type. In this paper, we present a study that focuses on investigating the impact of four user characteristics (perceptual speed, verbal working memory, visual working memory, and user expertise) on the effectiveness of two common data visualization techniques: bar graphs and radar graphs. Our results show that certain user characteristics do in fact have a significant effect on task efficiency, user preference, and ease of use. We conclude with a discussion of how our findings could be effectively used for an adaptive visualization system. Keywords: User characteristics, User Evaluation, Adaptive Information Visualization. IntroductionInformation visualization is a thriving area of research in the study of human/computer communication. Though the field has made substantial progress in measuring and formalizing visualization effectiveness, results and suggestions from the literature are sometimes inconclusive and conflicting [19]. We believe this may be attributed to the fact that existing visualizations are designed mostly around the target data set and associated task model, with little consideration for user differences. Both long term user characteristics (e.g., cognitive abilities and expertise) and short term factors (e.g., cognitive load and attention) have often been overlooked in the design of information visualizations, despite studies linking individual differences to visualization efficacy for search and navigation tasks [1,8], for information seeking tasks [7,25], as well as anecdotal evidence of diverse personal visualization preferences [3]. Our long term goal is to explore the possibilities of user-centered visualizations, which understand that different users have different visualization needs and abilities, and which can adapt to these differences. However, before adaptation strategies can be effectively specified, we believe that the influence of user characteristics on visua-

Individual user characteristics and information visualization

“…This result, however, is consistent with findings in [28], which showed that bar and radar graph expertise may only have significant effects on user visualization preference, but not on performance. These findings suggest that there might not be easily detectable differences in the visualization processing behaviors of experts and novices, as defined by our self-rated measures of expertise.…”

“…This finding provides encouraging evidence that this cognitive ability could be reliably detected in real time using gaze information. This result is particularly important for our long-term goal of designing user-adaptive visualizations, especially in light of previous studies, which showed that low perceptual speed can negatively affect task performance, in terms of both accuracy [3] as well as task completion time [28]. We have shown that perceptual speed influences AOI-specific gaze measures relating to the Legend, Labels and High AOIs.…”

“…Cognitive measures such as perceptual speed and visual memory have been shown to influence a user's ability to complete a task effectively [3,30]. These results were extended in a recent study [28] showing that perceptual speed and visual/verbal working memory influence not only task performance, but also a user's subjective preference for two different visualizations. While each of these studies clearly indicate that user differences should be considered in Infovis, they do not explain why or how these differences impact visualization processing, nor do they examine how this impact could be detected in real time.…”

“…We chose radar graphs because, although they are often considered inferior to bar graphs on common information seeking tasks [7], they are widely used for multivariate data. Furthermore, there are indications that radar graphs may be just as effective as bar graphs for more complex tasks [28].…”

“…Velez et al [30] found that a user's abilities for spatial reasoning (e.g., spatial orientation) were correlated with visualization comprehension. Similarly, Conati & Maclaren [3] and Toker et al [28] found that cognitive abilities such as perceptual speed, visual/verbal working memory and expertise can impact user performance or subjective preference with a given visualization.…”