A closer look at note taking in the co-located collaborative visual analytics process (original) (raw)
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Co-Located Collaborative Visual Analytics around a Tabletop Display
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Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.
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Sensemaking (i.e. the process of deriving meaning from complex information to make decisions) is often cited as an important and challenging activity for collaborative technology. A key element to the success of collaborative sensemaking is effective coordination and communication within the team. It requires team members to divide the task load, communicate findings and discuss the results. Sensemaking is one of the human activities involved in visual analytics (i.e. the science of analytical reasoning facilitated by interactive visual interfaces). The inherent complexity of the sensemaking process imposes many challenges for designers.
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In recent, numerous useful visual analytics tools have been designed to help domain experts solve analytical problems. However, most of the tools do not reflect the nature of solving real-world analytical tasks collaboratively because they have been designed for single users in desktop environments. In this paper, a complete visual analytics system is designed for solving real-world tasks having two integrated components: a single-user desktop system and an extended system suitable for a collaborative environment. Specifically, we designed a collaborative touch-table application (iPCA-CE) by adopting an existing single-user desktop analytical tool (iPCA). With the system, users can actively transit from individual desktop to shared collaborative environments without losing track of their analysis. They can also switch their analytical processes from collaborative to single-user workflows. To understand the usefulness of the system for solving analytical problems, we conducted a user...
A great corpus of studies reports empirical evidence of how information visualization supports comprehension and analysis of data. The benefits of visualization for synchronous group knowledge work, however, have not been addressed extensively. Anecdotal evidence and use cases illustrate the benefits of synchronous collaborative information visualization, but very few empirical studies have rigorously examined the impact of visualization on group knowledge work. We have consequently designed and conducted an experiment in which we have analyzed the impact of visualization on knowledge sharing in situated work groups. Our experimental study consists of evaluating the performance of 131 subjects (all experienced managers) in groups of 5 (for a total of 26 groups), working together on a real-life knowledge sharing task. We compare (1) the control condition (no visualization provided), with two visualization supports: (2) optimal and (3) suboptimal visualization (based on a previous survey). The facilitator of each group was asked to populate the provided interactive visual template with insights from the group, and to organize the contributions according to the group consensus. We have evaluated the results through both objective and subjective measures. Our statistical analysis clearly shows that interactive visualization has a statistically significant, objective and positive impact on the outcomes of knowledge sharing, but that the subjects seem not to be aware of this. In particular, groups supported by visualization achieved higher productivity, higher quality of outcome and greater knowledge gains. No statistically significant results could be found between an optimal and a suboptimal visualization though (as classified by the pre-experiment survey). Subjects also did not seem to be aware of the benefits that the visualizations provided as no difference between the visualization and the control conditions was found for the self-reported measures of satisfaction and participation. An implication of our study for information visualization applications is to extend them by using real-time group annotation functionalities that aid in the group sense making process of the represented data.
CoSpaces: Workspaces to Support Co-located Collaborative Visual Analytics
By design, interactive tabletops and surfaces provide numerous opportunities for data visualization and analysis. In information visualization, scientific visualization, and visual analytics, useful insights primarily emerge from interactive data exploration. Nevertheless, interaction research in these domains has largely focused on mouse-based interactions in the past, with little research on how interactive data exploration can benefit from interactive surfaces. These proceedings represent the results of the DEXIS 2011 Workshop on Data Exploration for Interactive Surfaces. It was held in conjunction with the ACM International Conference on Tabletops and Interactive Surfaces (ITS) in Kobe, Japan on November 13, 2011. The introduction summarizes the published papers of the workshop and points to results from workshop discussions. The remainder of the proceedings is made up of the position papers submitted to the workshop.
Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics
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Abstract Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies.
A model of synchronous collaborative information visualization
2003
Abstract We describe a model of the process by which people solve problems using information visualization systems. The model was based on video analysis of forty dyads who performed information visualization tasks in an experiment. We examined the following variables: focused questions vs. free data discovery, remote vs. collocated collaboration, and systems judged to have high and low transparency. The model describes the stages of reasoning and generating solutions with visual data.