Supporting Sensemaking during Collocated Collaborative Visual Analytics




Mahyar, Narges

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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. Therefore, providing effective tool support for collaborative sensemaking is a multifaceted and complex problem. Such tools should provide support for visualization as well as communication and coordination. Analysts need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate work activities amongst members of the team. Sharing externalizations (i.e. any information related to the course of analysis such as insights, hypotheses, to-do lists, reminders, etc recorded in the form of note/ annotation) could increase awareness and assist team members to better communicate and coordinate their work activities. However, we currently know very little about how to provide tool support for this sort of sharing. This thesis is structured around three major phases. It consists of a series of studies to better understand collaborative Visual Analytics (VA) processes and challenges, and empirically evaluate design ideas for supporting collaborative sensemaking. I investigate how collaborative sensemaking can be supported during visual analytics by a small team of collocated analysts. In the first phase of this research, I conducted an observational study to better understand the process of sensemaking during collaborative visual analytics as well as identify challenges and further requirements. This study enabled me to develop a deeper understanding of the collocated collaborative visual analytics process and activities involved. I found that record-keeping plays a critical role in the overall process of collaborative visual analytics. Record-keeping involves recording any information related to the analysis task including visualization snapshots, system states, notes, annotations and any other material for further analysis such as reminders and to-do lists. Based on my observations, I proposed a characterization of activities during collaborative visual analytics that encompasses record-keeping as one of the main activities. In addition, I characterized notes according to their content, scope, and usage, and described how they fit into a process of collaborative data analysis. Then, I derived guidelines to improve the design of record-keeping functionality for collocated collaborative visual analytics tools. One of the main design implications of my observational study was to integrate record-keeping functionality into a collaborative visual analytics tool. In order to examine how this feature should be integrated with current VA tools, in the second phase of this research, I designed, developed and evaluated a tool, CoSpaces (Collaborative Spaces), tailor-made for collocated collaborative data analysis on large interactive surfaces. Based on the result of a user study with this tool, I characterized users' actions on visual record-keeping as well as their key intentions for each action. In addition, I proposed further design guidelines such as providing various views of recorded material, showing manually saved rather than automatically saved items by default, enabling people to review collaborators' work unobtrusively, and automatically recommending items related to a user's analytical task. In the third phase, I took supporting record-keeping activities in the context of collaborative sensemaking a step further to investigate how this support should be designed to facilitate collaboration. To this end, I explored how automatic discovery and linking of common work can be employed within a ``collaborative thinking space'' (i.e. a space to enable analysts to record and organize findings, evidence, and hypotheses, also facilitate the process of sharing findings amongst collaborators), to facilitate synchronous collaborative sensemaking activities in visual analytics. The main goal of this phase was to provide an environment for analysts to record, organize, share and connect externalizations. I expected that this would increase awareness among team members and in turn would enhance communication and coordination of activities. I designed, implemented and evaluated a new tool, CLIP (Collaborative Intelligence Pad), that extends earlier thinking spaces by integrating new features that reveal relationships between collaborators' findings. Comparing CLIP versus a baseline tool demonstrated that linking collaborators' work led to significant improvement in analytical outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. Based on this study, I proposed design guidelines collaborative VA tools. In summary, I contribute an understanding for how analysts use VA tools during collocated collaboration. Through a series of observational user studies, I investigated how we can better support this complex process. More specifically, I empirically studied recording and sharing of analytical results. For this purpose, I implemented and evaluated two systems to be able to understand the effects of these tools on collaboration mechanics. These user studies along with various literature surveys on each specific topic resulted in a collection of guidelines for supporting and sharing externalizations. In addition, I proposed and evaluated several mechanisms to increase awareness among team members, resulting in more effective coordination and communication during the collaborative sensemaking process. The most novel contributions of this research are the identification and subsequent characterization of note taking behaviours as an important component of visual data exploration and analysis. Moreover, the design and evaluation of CLIP, providing preliminary evidence in support of automatically identifying and presenting relationships between collaborators' findings.



Sensemaking, Collaboration, Visual analytics, visualization