Number of co-authors:7
Number of publications with 3 favourite co-authors:Adam Fourney:4Michael Terry:4Steven Walczak:1
Richard Mann's 3 most productive colleagues in number of publications:Edward Lank:26Michael Terry:21Jaime Ruiz:15
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Publications by Richard Mann (bibliography)
Fourney, Adam, Mann, Richard and Terry, Michael (2011): Characterizing the usability of interactive applications through query log analysis. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1817-1826.
People routinely rely on Internet search engines to support their use of interactive systems: they issue queries to learn how to accomplish tasks, troubleshoot problems, and otherwise educate themselves on products. Given this common behavior, we argue that search query logs can usefully augment traditional usability methods by revealing the primary tasks and needs of a product's user population. We term this use of search query logs CUTS -- characterizing usability through search. In this paper, we introduce CUTS and describe an automated process for harvesting, ordering, labeling, filtering, and grouping search queries related to a given product. Importantly, this data set can be assembled in minutes, is timely, has a high degree of ecological validity, and is arguably less prone to self-selection bias than data gathered via traditional usability methods. We demonstrate the utility of this approach by applying it to a number of popular software and hardware systems.
© All rights reserved Fourney et al. and/or their publisher
Fourney, Adam, Mann, Richard and Terry, Michael (2011): Query-feature graphs: bridging user vocabulary and system functionality. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 207-216.
This paper introduces query-feature graphs, or QF-graphs. QF-graphs encode associations between high-level descriptions of user goals (articulated as natural language search queries) and the specific features of an interactive system relevant to achieving those goals. For example, a QF-graph for the GIMP graphics manipulation software links the query "GIMP black and white" to the commands "desaturate" and "grayscale." We demonstrate how QF-graphs can be constructed using search query logs, search engine results, web page content, and localization data from interactive systems. An analysis of QF-graphs shows that the associations produced by our approach exhibit levels of accuracy that make them eminently usable in a range of real-world applications. Finally, we present three hypothetical user interface mechanisms that illustrate the potential of QF-graphs: search-driven interaction, dynamic tooltips, and app-to-app analogy search.
© All rights reserved Fourney et al. and/or ACM Press
Fourney, Adam, Mann, Richard and Terry, Michael (2010): What can internet search engines "suggest" about the usage and usability of popular desktop applications?. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 417-418.
In this paper, we show how Internet search query logs can yield rich, ecologically valid data sets describing the common tasks and issues that people encounter when using software on a day-to-day basis. These data sets can feed directly into standard usability practices. We address challenges in collecting, filtering, and summarizing queries, and show how data can be collected at very low cost, even without direct access to raw query logs.
© All rights reserved Fourney et al. and/or their publisher
Walczak, Steven and Mann, Richard (2010): Utilization and Perceived Benefit for Diverse Users of Communities of Practice in a Healthcare Organization. In JOEUC, 22 (4) pp. 24-50.
Fourney, Adam, Terry, Michael and Mann, Richard (2010): Gesturing in the wild: understanding the effects and implications of gesture-based interaction for dynamic presentations. In: Proceedings of the HCI10 Conference on People and Computers XXIV 2010. pp. 230-240.
Driven by the increasing availability of low-cost sensing hardware, gesture-based input is quickly becoming a viable form of interaction for a variety of applications. Electronic presentations (e.g., PowerPoint, Keynote) have long been seen as a natural fit for this form of interaction. However, despite 20 years of prototyping such systems, little is known about how gesture-based input affects presentation dynamics, or how it can be best applied in this context. Instead, past work has focused almost exclusively on recognition algorithms. This paper explicitly addresses these gaps in the literature. Through observations of real-world practices, we first describe the types of gestures presenters naturally make and the purposes these gestures serve when presenting content. We then introduce Maestro, a gesture-based presentation system explicitly designed to support and enhance these existing practices. Finally, we describe the results of a real-world field study in which Maestro was evaluated in a classroom setting for several weeks. Our results indicate that gestures which enable direct interaction with slide content are the most natural fit for this input modality. In contrast, we found that using gestures to navigate slides (the most common implementation in all prior systems) has significant drawbacks. Our results also show how gesture-based input can noticeably alter presentation dynamics, often in ways that are not desirable.
© All rights reserved Fourney et al. and/or BCS
Ruiz, Jaime, Tausky, David, Bunt, Andrea, Lank, Edward and Mann, Richard (2008): Analyzing the Kinematics of Bivariate Pointing. In: Proceedings of the 2008 Conference on Graphics Interface May 28-30, 2008, Windsor, Ontario, Canada. pp. 251-258.
Despite the importance of pointing-device movement to efficiency in interfaces, little is known on how target shape impacts speed, acceleration, and other kinematic properties of motion. In this paper, we examine which kinematic characteristics of motion are impacted by amplitude and directional target constraints in Fitts-style pointing tasks. Our results show that instantaneous speed, acceleration, and jerk are most affected by target constraint. Results also show that the effects of target constraint are concentrated in the first 70% of movement distance. We demonstrate that we can discriminate between the two classes of target constraint using Machine Learning with accuracy greater than chance. Finally, we highlight future work in designing techniques that make use of target constraint to improve pointing efficiency in computer interfaces.
© All rights reserved Ruiz et al. and/or their publisher
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