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pdf VizScribe: A Visual Analytics Approach to Understand Designer Behavior ↗
Click to read abstract
Design protocol analysis is a technique to understand designers’ cognitive processes by analyzing sequences of observations on their behavior. These observations typically use audio, video, and transcript data in order to gain insights into the designer's behavior and the design process. The recent availability of sophisticated sensing technology has made such data highly multimodal, requiring more flexible protocol analysis tools. To address this need, we present VizScribe, a visual analytics framework that employs multiple coordinated multiple views that enable the viewing of such data from different perspectives. VizScribe allows designers to create, customize, and extend interactive visualizations for design protocol data such as video, transcripts, sketches, sensor data, and user logs. User studies where design researchers used VizScribe for protocol analysis indicated that the linked views and interactive navigation offered by VizScribe afforded the researchers multiple, useful ways to approach and interpret such multimodal data.
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pdf Mushaca: A 3-Degrees-of-Freedom Mouse Supporting Rotation ↗
Click to read abstract
Based on kinesiology research demonstrating that translation and rotation are inseparable actions in the physical world, we present Mushaca, a 3-degrees-of-freedom mouse that senses rotation in addition to traditional planar position. We present an optical realization of the Mushaca device based on two optical sensors and then evaluate the device through a series of controlled experiments. Our results show that rotation is indeed a useful input modality for a pointing device, and also give some insight into how users perceive the changing coordinate system of the rotating mouse and adapt to this change through kinesthetic learning.
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pdf Evaluating Social Navigation Visualization in Online Geographic Maps ↗
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Social navigation enables emergent collaboration between independent collaborators by exposing the behavior of each individual. This is a powerful idea for web-based visualization, where the work of one user can inform other users interacting with the same visualization. We present results from a crowdsourced user study evaluating the value of such social navigation cues for a geographic map service. Our results show significantly improved performance for participants who interacted with the map when the visual footprints of previous users were visible.
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doi TimeMatrix: Visualizing Temporal Social Networks Using Interactive Matrix-Based Visualizations ↗
Click to read abstract
Visualization plays a crucial role in understanding dynamic social networks at many different
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pdf 20 Years of Four HCI Conferences: A Visual Exploration ↗
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We present a visual exploration of the field of human–computer interaction (HCI) through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology, and Advanced Visual Interfaces and the IEEE Symposium on Information Visualization. This article describes many global and local patterns we discovered in this data set, together with the exploration process that produced them. Some expected patterns emerged, such as that---like most social networks---coauthorship and citation networks exhibit a power-law degree distribution, with a few widely collaborating authors and highly cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly referenced articles and that influential authors have distinct patterns of collaboration. An interesting sidelight is that methods from the HCI field---exploratory data analysis by information visualization and direct-manipulation interaction---proved useful for this analysis. They allowed us to take an open-ended, exploratory approach, guided by the data itself. As we answered our original questions, new ones arose; as we confirmed patterns we expected, we discovered refinements, exceptions, and fascinating new ones.