Engineering design typically occurs as a collaborative process situated in specific context such as computer-supported environments, however there is limited research examining the dynamics of design collaboration in specific contexts. In this study, drawing from situative learning theory, we developed two analytic lenses to broaden theoretical insights into collaborative design practices in computer-supported environments: (a) the role of spatial and material context, and (b) the role of social interactions. We randomly assigned participants to four conditions varying the material context (paper vs. tablet sketching tools) and spatial environment (private room vs commons area) as they worked collaboratively to generate ideas for a toy design task. We used wearable sociometric badges to automatically and unobtrusively collect social interaction data. Using partial least squares regression, we generated two predictive models for collaboration quality and creative fluency. We found that context matters materially to perceptions of collaboration, where those using collaboration-support tools perceived higher quality collaboration. But context matters spatially to creativity, and those situated in private spaces are more fluent in generating ideas than those in commons areas. We also found that interaction dynamics differ: synchronous interaction is important to quality collaboration, but reciprocal interaction is important to creative fluency. These findings provide important insights into the processual factors in collaborative design in computer-supported environments, and the predictive role of context and conversation dynamics. We discuss the theoretical contributions to computer-supported collaborative design, the methodological contributions of wearable sensor tools, and the practical contributions to structuring computer-supported environments for engineering design practice.

Current Computer-Aided Design (CAD) tools lack proper support for guiding novice users towards designs ready for fabrication. We propose Shape Structuralizer (SS), an interactive design support system that repurposes surface models into structural constructions using rods and custom 3D-printed joints. Shape Structuralizer embeds a recommendation system that computationally supports the user during design ideation by providing design suggestions on local refinements of the design. This strategy enables novice users to choose designs that both satisfy stress constraints as well as their personal design intent. The interactive guidance enables users to repurpose existing surface mesh models, analyze them in-situ for stress and displacement constraints, add movable joints to increase functionality, and attach a customized appearance. This also empowers novices to fabricate even complex constructs while ensuring structural soundness. We validate the Shape Structuralizer tool with a qualitative user study where we observed that even novice users were able to generate a large number of structurally safe designs for fabrication.

Despite its grounding in creativity techniques, merging multiple source sketches to create new ideas has received scant attention in design literature. In this paper, we identify the physical operations that in merging sketch components. We also introduce cognitive operations of reuse, repurpose, refactor, and reinterpret, and explore their relevance to creative design. To examine the relationship of cognitive operations, physical techniques, and creative sketch outcomes, we conducted a qualitative user study where student designers merged existing sketches to generate either an alternative design, or an unrelated new design. We compared two digital selection techniques: freeform selection, and a stroke-cluster-based "object select" technique. The resulting merge sketches were subjected to crowdsourced evaluation of these sketches, and manual coding for the use of cognitive operations. Our findings establish a firm connection between the proposed cognitive operations and the context and outcome of creative tasks. Key findings indicate that reinterpret cognitive operations correlate strongly with creativity in merged sketches, while reuse operations correlate negatively with creativity. Furthermore, freeform selection techniques are preferred significantly by designers. We discuss the empirical contributions of understanding the use of cognitive operations during design exploration, and the practical implications for designing interfaces in digital tools that facilitate creativity in merging sketches.

We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts-of-speech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.

We present CO-3DEATOR, a sketch-based collaborative 3D modeling system based on the notion of “team-first” ideation tools, where the needs and processes of the entire design team come before that of an individual designer. Co-3Deator includes two specific team-first features: a concept component hierarchy which provides a design representation suitable for multi-level sharing and reusing of design information, and a collaborative design explorer for storing, viewing, and accessing hierarchical design data during collaborative design activities. We conduct two controlled user studies, one with individual designers to elicit the form and functionality of the collaborative design explorer, and the other with design teams to evaluate the utility of the concept component hierarchy and design explorer towards collaborative design ideation. Our results support our rationale for both of the proposed team-first collaboration mechanisms and suggest further ways to streamline collaborative design.

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.

In this paper, we present a novel visualization framework for product and supply chain metadata in the context of redesign-related decision scenarios. Our framework is based on the idea of overlaying product-related metadata onto the interactive graph representations of a supply chain and its associated product architecture. By coupling environmental data with graph-based visualizations of product architecture, our framework provides a novel decision platform for expert designers. Here, the user can balance the advantages of a redesign opportunity and manage the associated risk on the product and supply chain. For demonstration, we present ViSER, an interactive visualization tool that provides an interface consisting of different mutually coordinated views providing multiple perspectives on a particular supply chain presentation. To explore the utility of ViSER, we conduct a domain expert exploration using a case study of peripheral computer equipment. Results indicate that ViSER enables new affordances within the decision making process for supply chain redesign.

Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge.

Direct-touch tablets are quickly replacing traditional pen-and-paper tools in many applications, but not in case of the designer’s sketchbook. In this paper, we explore the tradeoffs inherent in replacing such paper sketchbooks with digital tablets in terms of two major tasks: tracing and free-hand sketching. Given the importance of the pen for sketching, we also study the impact of using a blunt-and-soft-tipped capacitive stylus in tablet settings. We thus conducted experiments to evaluate three sketch media: pen-paper, finger-tablet, and stylus-tablet based on the above tasks. We analyzed the tracing data with respect to speed and accuracy, and the quality of the free-hand sketches through a crowdsourced survey. The pen-paper and stylus-tablet media both performed significantly better than the finger-tablet medium in accuracy, while the pen-paper sketches were significantly rated higher quality compared to both tablet interfaces. A follow-up study comparing the performance of this stylus with a sharp, hard-tip version showed no significant difference in tracing performance, though participants preferred the sharp tip for sketching.

Understanding the intent behind human gestures is a critical problem in the design of gestural interactions. A common method to observe and understand how users express gestures is to use elicitation studies. However, these studies require time-consuming analysis of user data to identify gesture patterns. Also, the analysis by humans cannot describe gestures in as detail as in data-based representations of motion features. In this paper, we present GestureAnalyzer, a system that supports exploratory analysis of gesture patterns by applying interactive clustering and visualization techniques to motion tracking data. GestureAnalyzer enables rapid categorization of similar gestures, and visual investigation of various geometric and kinematic properties of user gestures. We describe the system components, and then demonstrate its utility through a case study on mid-air hand gestures obtained from elicitation studies.

Juxtapoze is a clipart composition workflow that supports creative expression and serendipitous discoveries in the shape domain. We achieve creative expression by supporting a workflow of searching, editing, and composing: the user queries the shape database using strokes, selects the desired search result, and finally modifies the selected image before composing it into the overall drawing. Serendipitous discovery of shapes is facilitated by allowing multiple exploration channels, such as doodles, shape filtering, and relaxed search. Results from a qualitative evaluation show that Juxtapoze makes the process of creating image compositions enjoyable and supports creative expression and serendipity.

We present skWiki, a web application framework for collaborative creativity in digital multimedia projects, including text, hand-drawn sketches, and photographs. skWiki overcomes common drawbacks of existing wiki software by providing a rich viewer/editor architecture for all media types that is integrated into the web browser itself, thus avoiding dependence on client-side editors. Instead of files, skWiki uses the concept of paths as trajectories of persistent state over time. This model has intrinsic support for collaborative editing, including cloning, branching, and merging paths edited by multiple contributors. We demonstrate skWiki's utility using a qualitative, sketching-based user study.

Multitouch surfaces are becoming prevalent, but most existing technologies are only capable of detecting the user’s actual points of contact on the surface and not the identity, posture, and handedness of the user. In this paper, we define the concept of extended multitouch interaction as a richer input modality that includes all of this information. We further present a practical solution to achieve this on tabletop displays based on mounting a single commodity depth camera above a horizontal surface. This will enable us to not only detect when the surface is being touched, but also recover the user’s exact finger and hand posture, as well as distinguish between different users and their handedness. We validate our approach using two user studies, and deploy the technique in a scratchpad tool and in a pen + touch sketch tool.