Leah Findlater
University of Washington
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Featured researches published by Leah Findlater.
human factors in computing systems | 2010
Jon E. Froehlich; Leah Findlater; James A. Landay
Eco-feedback technology provides feedback on individual or group behaviors with a goal of reducing environmental impact. The history of eco-feedback extends back more than 40 years to the origins of environmental psychology. Despite its stated purpose, few HCI eco-feedback studies have attempted to measure behavior change. This leads to two overarching questions: (1) what can HCI learn from environmental psychology and (2) what role should HCI have in designing and evaluating eco-feedback technology? To help answer these questions, this paper conducts a comparative survey of eco-feedback technology, including 89 papers from environmental psychology and 44 papers from the HCI and UbiComp literature. We also provide an overview of predominant models of proenvironmental behaviors and a summary of key motivation techniques to promote this behavior.
IEEE Software | 2006
Gail C. Murphy; Mik Kersten; Leah Findlater
The Eclipse integrated development environment continues to gain popularity among Java developers. Our usage monitoring approach allows tool builders to sample how developers are using their tools in the wild. The data gathered about tool use can be used to prevent feature bloat and to evolve the environments according to user needs. Information about how developers work in a development environment can also provide a baseline for assessing new software development tools. We hope this report provides a start in defining which in formation to collect and distribute on an on going basis to help improve Eclipse and other similar platforms and tools
human factors in computing systems | 2004
Leah Findlater; Joanna McGrenere
Software applications continue to grow in terms of the number of features they offer, making personalization increasingly important. Research has shown that most users prefer the control afforded by an adaptable approach to personalization rather than a system-controlled adaptive approach. No study, however, has compared the efficiency of the two approaches. In a controlled lab study with 27 subjects we compared the measured and perceived efficiency of three menu conditions: static, adaptable and adaptive. Each was implemented as a split menu, in which the top four items remained static, were adaptable by the subject, or adapted according to the subjects frequently and recently used items. The static menu was found to be significantly faster than the adaptive menu, and the adaptable menu was found to be significantly faster than the adaptive menu under certain conditions. The majority of users preferred the adaptable menu overall. Implications for interface design are discussed.
human factors in computing systems | 2012
Mayank Goel; Leah Findlater; Jacob O. Wobbrock
The lack of tactile feedback on touch screens makes typing difficult, a challenge exacerbated when situational impairments like walking vibration and divided attention arise in mobile settings. We introduce WalkType, an adaptive text entry system that leverages the mobile devices built-in tri-axis accelerometer to compensate for extraneous movement while walking. WalkTypes classification model uses the displacement and acceleration of the device, and inference about the users footsteps. Additionally, WalkType models finger-touch location and finger distance traveled on the screen, features that increase overall accuracy regardless of movement. The final model was built on typing data collected from 16 participants. In a study comparing WalkType to a control condition, WalkType reduced uncorrected errors by 45.2% and increased typing speed by 12.9% for walking participants.
human factors in computing systems | 2008
Leah Findlater; Joanna McGrenere
Adaptive personalization, where the system adapts the interface to a users needs, has the potential for significant performance benefits on small screen devices. However, research on adaptive interfaces has almost exclusively focused on desktop displays. To explore how well previous findings generalize to small screen devices, we conducted a study with 36 subjects to compare adaptive interfaces for small and desktop-sized screens. Results show that high accuracy adaptive menus have an even larger positive impact on performance and satisfaction when screen real estate is constrained. The drawback of the high accuracy menus, however, is that they reduce the users awareness of the full set of items in the interface, potentially making it more difficult for users to learn about new features.
human factors in computing systems | 2012
Jon E. Froehlich; Leah Findlater; Marilyn Ostergren; Solai Ramanathan; Josh Peterson; Inness Wragg; Eric C. Larson; Fabia Fu; Mazhengmin Bai; Shwetak N. Patel; James A. Landay
Few means currently exist for home occupants to learn about their water consumption: e.g., where water use occurs, whether such use is excessive and what steps can be taken to conserve. Emerging water sensing systems, however, can provide detailed usage data at the level of individual water fixtures (i.e., disaggregated usage data). In this paper, we perform formative evaluations of two sets of novel eco-feedback displays that take advantage of this disaggregated data. The first display set isolates and examines specific elements of an eco-feedback design space such as data and time granularity. Displays in the second set act as design probes to elicit reactions about competition, privacy, and integration into domestic space. The displays were evaluated via an online survey of 651 North American respondents and in-home, semi-structured interviews with 10 families (20 adults). Our findings are relevant not only to the design of future water eco-feedback systems but also for other types of consumption (e.g., electricity and gas).
human factors in computing systems | 2013
Leah Findlater; Jon E. Froehlich; Kays Fattal; Jacob O. Wobbrock; Tanya Dastyar
Despite the apparent popularity of touchscreens for older adults, little is known about the psychomotor performance of these devices. We compared performance between older adults and younger adults on four desktop and touchscreen tasks: pointing, dragging, crossing and steering. On the touchscreen, we also examined pinch-to-zoom. Our results show that while older adults were significantly slower than younger adults in general, the touchscreen reduced this performance gap relative to the desktop and mouse. Indeed, the touchscreen resulted in a significant movement time reduction of 35% over the mouse for older adults, compared to only 16% for younger adults. Error rates also decreased.
human factors in computing systems | 2012
Leah Findlater; Ben Q. Lee; Jacob O. Wobbrock
Although many techniques have been proposed to improve text input on touch screens, the vast majority of this research ignores non-alphanumeric input (i.e., punctuation, symbols, and modifiers). To support this input, widely adopted commercial touch-screen interfaces require mode switches to alternate keyboard layouts for most punctuation and symbols. Our approach is to augment existing ten-finger QWERTY keyboards with multi-touch gestural input that can exist as a complement to the moded-keyboard approach. To inform our design, we conducted a study to elicit user-defined gestures from 20 participants. The final gesture set includes both multi-touch and single-touch gestures for commonly used non-alphanumeric text input. We implemented and conducted a preliminary evaluation of a touch-screen keyboard augmented with this technique. Findings show that using gestures for non-alphanumeric input is no slower than using keys, and that users strongly prefer gestures to a moded-keyboard interface.
human factors in computing systems | 2011
Leah Findlater; Jacob O. Wobbrock; Daniel Wigdor
Touch screen surfaces large enough for ten-finger input have become increasingly popular, yet typing on touch screens pales in comparison to physical keyboards. We examine typing patterns that emerge when expert users of physical keyboards touch-type on a flat surface. Our aim is to inform future designs of touch screen keyboards, with the ultimate goal of supporting touch-typing with limited tactile feedback. To study the issues inherent to flat-glass typing, we asked 20 expert typists to enter text under three conditions: (1) with no visual keyboard and no feedback on input errors, then (2) with and (3) without a visual keyboard, but with some feedback. We analyzed touch contact points and hand contours, looking at attributes such as natural finger positioning, the spread of hits among individual keys, and the pattern of non-finger touches. We also show that expert typists exhibit spatially consistent key press distributions within an individual, which provides evidence that eyes-free touch-typing may be possible on touch surfaces and points to the role of personalization in such a solution. We conclude with implications for design.
human factors in computing systems | 2012
Leah Findlater; Jacob O. Wobbrock
Although typing on touchscreens is slower than typing on physical keyboards, touchscreens offer a critical potential advantage: they are software-based, and, as such, the keyboard layout and classification models used to interpret key presses can dynamically adapt to suit each users typing pattern. To explore this potential, we introduce and evaluate two novel personalized keyboard interfaces, both of which adapt their underlying key-press classification models. The first keyboard also visually adapts the location of keys while the second one always maintains a visually stable rectangular layout. A three-session user evaluation showed that the keyboard with the stable rectangular layout significantly improved typing speed compared to a control condition with no personalization. Although no similar benefit was found for the keyboard that also offered visual adaptation, overall subjective response to both new touchscreen keyboards was positive. As personalized keyboards are still an emerging area of research, we also outline a design space that includes dimensions of adaptation and key-press classification features.