Steven Houben

Zhuoyi Cheng, Steven Houben

Sensemaking is an important preceding step for activities like consensus building and decision-making. When groups of people make sense of large amounts of information, their understanding gradually evolves from vague to clear. During this process when reaching a conclusion is still premature, if people are presented with others' insights, they may be directed to focus on that specific perspective without adequate verification. We argue that similar phenomena may also exist in AI-assisted sensemaking, in which AI will usually be the one that presents insight prematurely when users' understandings are still vague and ill-formed. In this paper, we raised three questions that are worth deliberation before exploiting AI to assist in collaborative sensemaking in practice, and discussed possible reasons that may lead users to opt for insights from AI.

Steven Jeuris, Paolo Tell, Steven Houben et al.

Most window management systems support multitasking by allowing users to open, resize, position, and switch between application windows. Although multitasking has become a way of life for most knowledge workers, our current understanding of how users use window management features to switch between multiple tasks---which may comprise multiple application windows---is limited. In this paper, we present a study providing an in-depth analysis of how task switching is supported in Windows 7. As part of analysis, we developed an interface-agnostic classification of common task switching operations supported by window managers which can be used to quantify the time spent on each constituting action. Our study shows that task switching is a time intensive activity and highlights the dominant actions that contribute to task switch time. Furthermore, our classification highlights the specific operations that are optimized by more recent and experimental window managers and allows identifying opportunities for design that could further reduce the overhead of switching between tasks.