There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

Menus are a primary control in current interfaces, but there has been relatively little theoretical work to model their performance. We propose a model of menu performance that goes beyond previous work by incorporating components for Fitts' Law pointing time, visual search time when novice, Hick-Hyman Law decision time when expert, and for the transition from novice to expert behaviour. The model is able to predict performance for many different menu designs, including adaptive split menus, items with different frequencies and sizes, and multi-level menus. We tested the model by comparing predictions for four menu designs (traditional menus, recency and frequency based split menus, and an adaptive 'morphing' design) with empirical measures. The empirical data matched the predictions extremely well, suggesting that the model can be used to explore a wide range of menu possibilities before implementation.

This paper provides an empirical characterisation of user actions at the web browser. The study is based on an analysis of four months of logged client-side data that describes user actions with recent versions of Netscape Navigator. In particular, the logged data allows us to determine the title, URL and time of each page visit, how often they visited each page, how long they spent at each page, the growth and content of bookmark collections, as well as a variety of other aspects of user interaction with the web. The results update and extend prior empirical characterisations of web use. Among the results we show that web page revisitation is a much more prevalent activity than previously reported (approximately 81% of pages have been previously visited by the user), that most pages are visited for a surprisingly short period of time, that users maintain large (and possibly overwhelming) bookmark collections, and that there is a marked lack of commonality in the pages visited by different users. These results have implications for a wide range of web-based tools including the interface features provided by web-browsers, the design of caching proxy servers, and the design of efficient web-sites.

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Abstract.Recently there has been an increase in research towards using hand gestures for interaction in the field of Augmented Reality (AR). These works have primarily focused on researcher designed gestures, while little is known about user preference and behavior for gestures in AR. In this paper, we present our guessability study for hand gestures in AR in which 800 gestures were elicited for 40 selected tasks from 20 participants. Using the agreement found among gestures, a user-defined gesture set was created to guide designers to achieve consistent user-centered gestures in AR. Wobbrock's surface taxonomy has been extended to cover dimensionalities in AR and with it, characteristics of collected gestures have been derived. Common motifs which arose from the empirical findings were applied to obtain a better understanding of users' thought and behavior. This work aims to lead to consistent user-centered designed gestures in AR.