Creativity support tools are set an especially difficult task when they are applied to art/science collaboration. Not because of any fundamental incompatibility between the disciplines, but because creativity support tools are rarely supple enough to manage dramatically shifting requirements at various stages of design or handle the diversity of artifacts that might be generated. Traditional methods of evaluation of collaborative support tools may not address these aspects. This workshop aims to examine three specific areas open to expanded modes of evaluation; the social aspects of tools and tool use, how artifacts are created and manipulated in support tools, and how the expanding contexts of art/science collaborations may be rapidly changing support tool requirements.

Many collaborative design tools may suffer from being too generic to address the specific complexities inherent in multidisciplinary collaboration. We provide accounts of several multidisciplinary HCI courses at our institution, elaborating on the challenges student teams face when integrating design practice from a wide variety of disciplines. Of particular interest are the distinct approaches that these multidisciplinary teams adopt that differ from more common forms of collaborative design. We suggest reasons for the poor rate of adoption of existing collaborative support tools and outline specific suggestions for directions in both ethnographic studies of multidisciplinary collaboration and collaborative systems design.

To contribute to systems that reason about human attention, our work empirically demonstrates how a user's mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.

User attention is a scarce resource, and users are susceptible to interruption overload. Systems do not reason about the effects of interrupting a user during a task sequence. In this study, we measure effects of interrupting a user at different moments within task execution in terms of task performance, emotional state, and social attribution. Task models were developed using event perception techniques, and the resulting models were used to identify interruption timings based on a user's predicted cognitive load. Our results show that different interruption moments have different impacts on user emotional state and positive social attribution, and suggest that a system could enable a user to maintain a high level of awareness while mitigating the disruptive effects of interruption. We discuss implications of these results for the design of an attention manager.

Information gathering from multimedia retrieval systems is aided by effective visualization, but the degree to which visualization is effective depends in part on the way the context of the results is presented. When relationships represent media rich connections, static visualization alone may not be enough. This work explores how to represent context and utilize multimedia to convey a more accurate sense of search results. As a representative case, we explore various presentations of social networks formed by expert opinions of musical artist similarity. Our work extends research in information visualization and music retrieval to create a multimedia search experience. Three interactive presentation styles are used; graph-based 2D, Desktop 3D (VRML), and CAVE (immersive Virtual Reality). Visual models are augmented with spatial audio in 3D, and hyperlinks to sound files in 2D. Results of a preliminary user study of these styles are discussed along with implications for recommender system design.