Putting Icons in Context: The Influence of Contextual Information on the Usability of Icons

Previous research has been driven by the idea that a good icon is one which consists of a form that is instantly recognisable as representing the underlying referent. As a result many of the design decisions suggested have been based round this premiss. However, the empirical method used to support these design decisions has often tended to ignore two factors of everyday interface usage that have an important influence on the ability of an icon to communicate its meaning. The first factor that most researchers tended to ignore was that an icon is usually displayed simultaneously with a number of other icons. Rarely will you find icons displayed in isolation, yet many methodologies ask subjects to judge an icon's ability to communicate its meaning without any additional information from the interface to support it. A second issue is that, in everyday usage, most users interact with icons over an extended period of time. Therefore it is not necessarily essential that the icon's meaning should be easy to guess, only that it be particularly easy to learn. The ultimate aim of this thesis, therefore, is to suggest that icon research should no longer be concerned with measuring the degree to which a particular representational form of a solitary icon can increase performance on a subject's initial exposure to the icon. Instead research should be widened to consider how attributes in the interface interact, and how this interaction may vary over time as user knowledge of the interface increases. A total of six experiments were performed. Each experiment had four conditions, manipulating the abstractness of icon shape and consistency of icon position. After a training period conditions changed without warning and the conclusions depended on whether or not performance was disrupted by the change. Results from the experiments suggested overwhelmingly that issues such as what attributes users relied upon, and when they relied upon them, were far more complicated than initially predicted. The conclusions of the thesis question the generalisability of many long held assumptions about icon design. Findings show that once an icon is placed into an interface it is difficult to predict which of its attributes the user will rely on. It seems that this will not be determined by how representational the attribute is, but rather how discriminate it is in comparison to other attributes, once the icon is placed within a particular interface. Results also show that users are aware of multiple attributes apparent in the interface and may switch the attribute that they rely on several times within the entire user learning curve. However, it is impossible to say how much users learn, since the experimental data has shown that learning appears to continue after the classic performance measures of reaction time and error rate have reached asymptote. Finally, the results highlight the flaws in current evaluation paradigms; namely that most are guilty of testing icons in isolation and only in the initial stages of the learning curve. The experimental results, combined with the extensive literature review in Chapter 2, elicit a number of interesting future research questions, as well as providing designers with some general suggestions as rules-of-thumb for interface design. The thesis concludes by elaborating on these points

Consistency and usability

This thesis is divided into two main parts. In the first a multi-component framework for usability is outlined. The components - guessability, learnability, experienced user performance, system potential, and re-usability - describe how users' performance on a task changes with experience, and each is associated with a different part of a learning curve. Definitions of the components are offered, as are examples of where each may be particularly important. The potential advantages of the framework are then discussed. An experimental methodology and criteria based analysis method for quantifying the components of usability is proposed. This is demonstrated in the evaluation of a video cassette recorder. Non-experimental alternatives to this methodology are also considered. The second part of the thesis is about the issue of consistency. A distinction between two types of consistency - set compatibility and rule compatibility - is proposed. Set compatibility is concerned with inter-task associations, whilst rule compatibility concerns the prior association of tasks and action-rules. Predictions are made about the effects of each type of consistency on the various components of usability, and these are tested in the context of a study of the invocation of menu commands. Results indicated that rule compatibility had the greater effect on early interactions, whilst set compatibility was more salient later on. A series of further studies is then reported, the aim of which was to investigate whether these effects were general across types and levels of interface, and other levels of task. Results mainly, but not entirely, indicated that they were. Data from a more `ecologically valid' usability evaluation was re-analysed, to investigate whether the effects of consistency are important outside of artificial and tightly controlled experiments. Apparently they are - almost half of the difficulties encountered during users' early interactions with a commercially available word processor could be attributed to either set or rule incompatibilities

The development of Spatial Intelligent Agents With Geographic Information Systems

The manipulation of geographic information (GI) has been considered, by most of its users, as a very complex process and has involved the development of specific applications called geographic information systems (GIS). A new subdiscipline of Information Science called Geographic Information Science (GISc) has been proposed by the GIS research community on the grounds that there are specific characteristics not only to GI but also to the processes involved in its manipulation. The thesis draws on several research issues which are part of the agenda in GISc: The complexity of handling spatial/geographic information, spatial reasoning in dynamical systems, integration of several types of application, human-computer interaction and spatio-temporal issues. In this context, this dissertation proposes the application of a new computational paradigm, intelligent agents in GISc. Intelligent agents are “computational systems that inhabit some complex dynamic environment, sense and act autonomously, and by doing so realise a set of goals or tasks for which they are designed “ (Maes, 1995). The aim of this dissertation is to analyse the potential of research in intelligent agents in GISc and to explore the use of simple learning techniques to improve the adaptability of spatial intelligent agents. The thesis involves the following objectives: to analyse the needs of research in GISc in the areas of reasoning about geographic space; to study the potential of intelligent agents in that area of research; to explore the use of simple learning techniques to improve the adaptability of intelligent agents for geographic information; and to explore the implementation environments of GIS software for the integration of intelligent agent systems. The primary contributions of this research are three case studies which use intelligent agents in a spatial or geographic context: a simple non-adaptive interface assistant for the printing and plotting tool of Smallworld GIS; an intelligent assistant that uses memory-based reasoning to identify and locate specific-purpose geographic information; a simulation of a car park where agents are cars that use reinforcement learning techniques to improve their parking performance