Abstract. We present Adaptive Pointing, a novel approach to addressing the common problem of accuracy when using absolute pointing devices for distant interaction. First, we discuss extensively some related work concerning the problem-domain of pointing accuracy when using absolute or relative pointing devices. As a result, we introduce a novel classification scheme to more clearly discriminate between different approaches. Second, the Adaptive Pointing technique is presented and described in detail. The intention behind this approach is to improve pointing performance for absolute input devices by implicitly adapting the Control-Display gain to the current user's needs without violating users' mental model of absolute-device operation. Third, we present an experiment comparing Adaptive Pointing with pure absolute pointing using a laser-pointer as an example of an absolute device. The results show that Adaptive Pointing results in a significant improvement compared with absolute pointing in terms of movement time (19%), error rate (63%), and user satisfaction.
In this paper we present Pocket Bee, a multi-modal diary tool that allows researchers to remotely collect rich and indepth data in the field. Based on the Android smart phone platform, we especially focused on an easy to use user interface. We introduce the notion of core questions that serve as cognitive triggers for pre-defined events. Multiple modalities allow participants to compose notes in the most appropriate and convenient way. Instant network synchronization allows researchers to view and analyze the data on-the-fly while also being able to create new tasks or questionnaires during an ongoing study. These can also be linked to certain trigger events, such as time and date. Thereby, Pocket Bee supports diary and Experience Sampling (ESM) studies. The system was developed in a user-centered design process and its potential value is described in a scenario of use illustrating an upcoming study.
In this paper we present our experiences with longitudinal study designs for input device evaluation. In this domain, analyzing learning is currently the main reason for applying longitudinal designs. We will shortly discuss related research questions and outline two case studies in which we used different approaches to address this issue. Finally, we will point out future research tasks in the context of longitudinal evaluation methods.
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