Modern window-based user interface systems generate user interface events as natural products of their normal operation. Because such events can be automatically captured and because they indicate user behavior with respect to an application's user interface, they have long been regarded as a potentially fruitful source of information regarding application usage and usability. However, because user interface events are typically voluminos and rich in detail, automated support is generally required to extract information at a level of abstraction that is useful to investigators interested in analyzing application usage or evaluating usability. This survey examines computer-aided techniques used by HCI practitioners and researchers to extract usability-related information from user interface events. A framework is presented to help HCI practitioners and researchers categorize and compare the approaches that have been, or might fruitfully be, applied to this problem. Because many of the techniques in the research literature have not been evaluated in practice, this survey provides a conceptual evaluation to help identify some of the relative merits and drawbacks of the various classes of approaches. Ideas for future research in this area are also presented. This survey addresses the following questions: How might user interface events be used in evaluating usability? How are user interface events related to other forms of usability data? What are the key challenges faced by investigators wishing to exploit this data? What approaches have been brought to bear on this problem and how do they compare to one another? What are some of the important open research questions in this area?

We present a study of complex visualization usage by expert meteorological forecasters. We performed a protocol analysis and examined the types of visualizations they examined. We present evidence for how experts are able to make use of complex visualizations. Our #ndings suggest that users of complex visualizations create qualitativemental models from which they can then generate quantitative information. In order to build their qualitative mental models, forecasters integrated information across multiple visualizations and extracted primarily qualitative information from visualizations in a goal-directed manner. We discuss both theoretical and practical implications of this study. Introduction Everywhere you look, newspapers and banners declare this the #Information Age." Scientists, engineers, and weather forecasters #just to name a few# examine extremely large amounts of data on a daily basis. The visualizations that they examine have so much data that it is obvious that...

We present a case study that tracks usability problems predicted with six usability evaluation methods (Claims Analysis, Cognitive Walkthrough, GOMS, Heuristic Evaluation, User Action Notation, and simply reading the specification) through a development process. We assess the methods' predictive power by comparing the predictions to the results of user tests. We assess the methods' persuasive power by seeing how many problems led to changes in the implemented code. We assess design-change effectiveness by user testing the resulting new versions of the system. We conclude that predictive methods are not as effective as the HCI field would like and discuss directions for future research.

DIVA supports exploratory data analysis of multimedia streams, enabling users to visualize, explore and evaluate patterns in data that change over time. The underlying stream algebra provides the mathematical basis for operating on diverse kinds of streams. The streamer visualization technique provides a smooth transition between spatial and temporal views of the data. Mapping source and presentation streams into a two-dimensional space provides users with a direct manipulation, nontemporal interface for viewing and editing streams. DIVA was developed to help us analyze both qualitative and quantitative data collected in our research with French air traffic controllers, including video of controllers at work, audio records of telephone, radio and other conversations, output from tools such as RADAR, and coded logs based on our observations. Although our emphasis is on exploratory data analysis, DIVA's stream architecture should prove useful for a wide variety of multimedia application.

Geographic Visualization, sometimes called cartographic visualization, is a form of information visualization in which principles from cartography, geographic information systems (GIS), Exploratory Data Analysis (EDA), and information visualization more generally are integrated in the development and assessment of visual methods that facilitate the exploration, analysis, synthesis, and presentation of georeferenced information. We report on development and use of one component of a prototype GVis environment designed to facilitate exploration, by domain experts, of time series multivariate georeferenced health statistics. Emphasis is on how manipulable dynamic GVis tools may facilitate visual thinking, pattern noticing, and hypothesis generation. The prototype facilitates the highlighting of data extremes, examination of change in geographic patterns over time, and exploration of similarity among georeferenced variables. A qualitative exploratory analysis of verbal protocols and transaction logs is used to characterize system use. Evidence produced through the characterization highlights differences among experts in data analysis strategies (particularly in relation to the use of attribute �focusing " combined with time series animation) and corresponding differences in success at noticing spatiotemporal patterns..

This article describes and evaluates a class of methods for performing automated analysis of eye-movement protocols. Although eye movements have become increasingly popular as a tool for investigating user behavior, they can be extremely difficult and tedious to analyze. In this article we propose an approach to automating eye-movement protocol analysis by means of tracing—relating observed eye movements to the sequential predictions of a process model. We present three tracing methods that provide fast and robust analysis and alleviate the equipment noise and individual variability prevalent in typical eye-movement protocols. We also describe three applications of the tracing methods that demonstrate how the methods facilitate the use of eye movements in the study of user behavior and the inference of user intentions. 1.

This article investigates how and why people remember the existence of hidden information. To obtain data on this kind of memory phenomenon, we observed an experienced programmer doing her own work at her own computer. The programmer's interaction with the computer generates much more information than fits on the screen at once. Most of this information is hidden, scrolled out of the way by the programming environment to make Direct all correspondence to: Erik M. Altmann, George Mason University, Human Factors & Applied Cognition, Mailstop 2E5, Fairfax, VA 22030 USA; E-Mail: altmann@gmu.edu

This thesis presents a computational framework for the automatic recognition and prediction of different kinds of human behaviors from video cameras and other sensors, via perceptually intelligent systems that automatically sense and correctly classify human behaviors, by means of Machine Perception and Machine Learning techniques. In the thesis I develop the statistical machine learning algorithms (dynamic graphical models) necessary for detecting and recognizing individual and interactive behaviors. In the case of the interactions two Hidden Markov Models (HMMs) are coupled in a novel architecture called Coupled Hidden Markov Models (CHMMs) that explicitly captures the interactions between them. The algorithms for learning the parameters from data as well as for doing inference with those models are developed and described. Four systems that experimentally evaluate the proposed paradigm are presented: (1) LAFTER, an automatic face detection and tracking system with facial expression recognition; (2) a Tai-Chi gesture recognition system; (3) a pedestrian surveillance system that recognizes typical human to human interactions; (4) and a SmartCar for driver maneuver recognition. These systems capture human behaviors of different nature and increasing complexity: first, isolated, single-user facial expressions, then, two-hand gestures and human-to-human interactions,...

this report. georeferenced data

policies, either expressed or implied, of the NSF or the U.S. government.