Joanna McGrenere

A comparison of static, adaptive, and adaptable menus

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.

The participatory design of a sound and image enhanced daily planner for people with aphasia

Aphasia is a cognitive disorder that impairs speech and language. From interviews with aphasic individuals, their caregivers, and speech-language pathologists, the need was identified for a daily planner that allows aphasic users to independently manage their appointments. We used a participatory design approach to develop ESI Planner (the Enhanced with Sound and Images Planner) for use on a PDA and subsequently evaluated it in a lab study. This methodology was used in order to achieve both usable and adoptable technology. In addition to describing our experience in designing ESI Planner, two main contributions are provided: general guidelines for working with special populations in the development of technology, and design guidelines for accessible handheld technology.

Impact of screen size on performance, awareness, and user satisfaction with adaptive graphical user interfaces

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.

Ephemeral adaptation: the use of gradual onset to improve menu selection performance

We introduce ephemeral adaptation, a new adaptive GUI technique that improves performance by reducing visual search time while maintaining spatial consistency. Ephemeral adaptive interfaces employ gradual onset to draw the users attention to predicted items: adaptively predicted items appear abruptly when the menu is opened, but non-predicted items fade in gradually. To demonstrate the benefit of ephemeral adaptation we conducted two experiments with a total of 48 users to show: (1) that ephemeral adaptive menus are faster than static menus when accuracy is high, and are not significantly slower when it is low and (2) that ephemeral adaptive menus are also faster than adaptive highlighting. While we focused on user-adaptive GUIs, ephemeral adaptation should be applicable to a broad range of visually complex tasks.

Designing haptic icons to support collaborative turn-taking

This paper describes research exploring the use of haptics to support users collaborating remotely in a single-user shared application. Mediation of turn-taking during remote collaboration provides a context to explore haptic affordances for background communication as well as control negotiation in remote collaboration: existing turn-taking protocols are rudimentary, lacking many communication cues available in face-to-face collaboration. We therefore designed a custom turn-taking protocol that allows users to express different levels of urgency in their request for control from a collaborator; state of control and requests are communicated by touch, with the intent of offloading visual attention. To support it, we developed a set of haptic icons, tangible stimuli to which specific meanings have been assigned. Because we required an icon set which could be utilized with specified, varying levels of intrusiveness in real attentionally challenged situations, we used a perceptually guided procedure that consisted of four steps: initial icon set design, perceptual refinement, validation of learnability and effectiveness under workload, and deployment in an application simulation. We found that our haptic icons could be learned to a high degree of accuracy in under 3min and remained identifiable even under significant cognitive workload. In an exploratory observational study comparing haptic, visual, and combined haptic and visual support for our protocol, participants overall preferred the combined multi-modal support, and in particular preferred the haptic support for control changes and the visual support for displaying state. In their control negotiation, users clearly utilized the option of requesting with graded urgency. The three major contributions in this paper are: (1) the introduction and first case study using a systematic process for refining and evaluating haptic icons for background communication in a primarily visual application; (2) the usability observed for a particular set of icons designed with that process; and (3) the introduction of an urgency-based turn-taking protocol and a comparison of haptic, visual and multi-modal support of our implementation of that protocol.

Supporting interface customization using a mixed-initiative approach

We describe a mixed-initiative framework designed to support the customization of complex graphical user interfaces. The framework uses an innovative form of online GOMS analysis to provide the user with tailored customization suggestions aimed at maximizing the users performance with the interface. The suggestions are presented non-intrusively, minimizing disruption and allowing the user to maintain full control. The framework has been applied to a general user-productivity application. A formal user evaluation of the system provides encouraging evidence that this mixed-initiative approach is preferred to a purely adaptable alternative and that the systems suggestions help improve task performance.

An evaluation of pan & zoom and rubber sheet navigation with and without an overview

We present a study that evaluates conventional Pan and Zoom Navigation and Rubber Sheet Navigation, a rectilinear Focus+Context technique. Each of the two navigation techniques was evaluated both with and without an overview. All interfaces guaranteed that regions of interest would remain visible, at least as a compressed landmark, independent of navigation actions. Interfaces implementing these techniques were used by 40 subjects to perform a task that involved navigating a large hierarchical tree dataset and making topological comparisons between nodes in the tree. Our results show that Pan and Zoom Navigation was significantly faster and required less mental effort than Rubber Sheet Navigation, independent of the presence or absence of an overview. Also, overviews did not appear to improve performance, but were still perceived as beneficial by users. We discuss the implications of our task and guaranteed visibility on the results and the limitations of our study, and we propose preliminary design guidelines and recommendations for future work.

How Older Adults Learn to Use Mobile Devices: Survey and Field Investigations

Mobile computing devices, such as smart phones, offer benefits that may be especially valuable to older adults (age 65+). Yet, older adults have been shown to have difficulty learning to use these devices. In the research presented in this article, we sought to better understand how older adults learn to use mobile devices, their preferences and barriers, in order to find new ways to support them in their learning process. We conducted two complementary studies: a survey study with 131 respondents from three age groups (20--49, 50--64, 65+) and an in-depth field study with 6 older adults aged 50+. The results showed, among other things, that the preference for trial-and-error decreases with age, and while over half of older respondents and participants preferred using the instruction manual, many reported difficulties using it. We discuss implications for design and illustrate these implications with an example help system, Help Kiosk, designed to support older adults’ learning to use mobile devices.

Matching attentional draw with utility in interruption

This research examines a design guideline that aims to increase the positive perception of interruptions. The guideline advocates matching the amount of attention attracted by an interruptions notification method (attentional draw) to the utility of the interruption content. Our first experiment examined a set of 10 visual notification signals in terms of their detection times and established a set of three significantly different signals along the spectrum of attentional draw. Our second experiment investigated matching these different signals to interruption content with different levels of utility. Results indicate that the matching strategy decreases annoyance and increases perception of benefit compared to a strategy that uses the same signal regardless of interruption utility, with no significant impact on workload or performance. Design implications arising from the second experiment as well as recommendations for future work are discussed.

Multi-Layered Interfaces to Improve Older Adults’ Initial Learnability of Mobile Applications

Mobile computing devices can offer older adults (ages 65+) support in their daily lives, but older adults often find such devices difficult to learn and use. One potential design approach to improve the learnability of mobile devices is a Multi-Layered (ML) interface, where novice users start with a reduced-functionality interface layer that only allows them to perform basic tasks, before progressing to a more complex interface layer when they are comfortable. We studied the effects of a ML interface on older adults’ performance in learning tasks on a mobile device. We conducted a controlled experiment with 16 older (ages 65--81) and 16 younger participants (age 21--36), who performed tasks on either a 2-layer or a nonlayered (control) address book application, implemented on a commercial smart phone. We found that the ML interface’s Reduced-Functionality layer, compared to the control’s Full-Functionality layer, better helped users to master a set of basic tasks and to retain that ability 30 minutes later. When users transitioned from the Reduced-Functionality to the Full-Functionality interface layer, their performance on the previously learned tasks was negatively affected, but no negative impact was found on learning new, advanced tasks. Overall, the ML interface provided greater benefit for older participants than for younger participants in terms of task completion time during initial learning, perceived complexity, and preference. We discuss how the ML interface approach is suitable for improving the learnability of mobile applications, particularly for older adults.