A new perspective on design thinking and design practice: beyond products and projects, toward participatory design things. Design Things offers an innovative view of design thinking and design practice, envisioning ways to combine creative design with a participatory approach encompassing aesthetic and democratic practices and values. The authors of Design Things look at design practice as a mode of inquiry that involves people, space, artifacts, materials, and aesthetic experience, following the process of transformation from a design concept to a thing. Design Things, which grew out of the Atelier (Architecture and Technology for Inspirational Living) research project, goes beyond the making of a single object to view design projects as sociomaterial assemblies of humans and artifacts—“design things.” The book offers both theoretical and practical perspectives, providing empirical support for the authors' conceptual framework with field projects, case studies, and examples from professional practice. The authors examine the dynamics of the design process; the multiple transformations of the object of design; metamorphing, performing, and taking place as design strategies; the concept of the design space as “emerging landscapes”; the relation between design and use; and the design of controversial things.

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user's evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users' task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory-comparison, knowledge acquisition, planning-and 3 lookup tasks-factfinding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems. IntroductionSearch activities are commonly divided into two broad categories: lookup and exploratory (Marchionini, 2006). Lookup search is by far the better understood and assumed to have precise search goals. The predominant design goal in information retrieval (IR) systems has been fast and accurate completion of lookup searches. Exploratory search is presently thought to center around the acquisition of new knowledge and considered to be challenging for the user (White & Roth, 2009). Although there has been a lot of research on understanding exploratory search, there are many open questions when it comes to the design of IR systems that provide tailored and adaptive support. One of the key problems is how we can make an IR system automatically distinguish the two categories of search in the course of a search session (Belkin, 2008). In this article, we look into if, and how well, we can tell apart lookup and exploratory search activities from properties that IR systems can easily observe.It is difficult to separate exploratory and lookup search in IR systems. This is because currently there is a gap between our knowledge in exploratory search behaviors and requirements of IR system design. First, many studies compared the exploratory and lookup searches by cognitive strategies only (J. Kim, 2009;Thatcher, 2008 activity: learning or knowledge acquisition. However, it is held that exploratory search involves many subcategories of search activities (Marchionini, 2006;White & Roth, 2009). Third, many studies that attempt to distinguish between different task types only consider web search behaviors but not behaviors specific to IR system use (Liu et al., 2010b). There are marked differences between web searching and...

No abstract