Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces

Bernstein

2013

MISQ

163

Adapting user interfaces to a user's cultural background can increase satisfaction, revenue, and market share. Conventional approaches to catering for culture are restricted to adaptations for specific countries and modify only a limited number of interface components, such as the language or date and time formats. We argue that a more comprehensive personalization of interfaces to cultural background is needed to appeal to users in expanding markets. This paper introduces a low-cost, yet efficient method to achieve this goal: cultural adaptivity. Culturally adaptive interfaces are able to adapt their look and feel to suit visual preferences. In a design science approach, we have developed a number of artifacts that support cultural adaptivity, including a prototype web application. We evaluate the efficacy of the prototype's automatically generated interfaces by comparing them with the preferred interfaces of 105 Rwandan, Swiss, Thai, and multicultural users. The findings demonstrate the feasibility of providing users with interfaces that correspond to their cultural preferences in a novel yet effective manner.

Section: Adaptive User Interfacesmentioning

confidence: 92%

Section: Acquiring and Storing Information About A User's Cultural Bamentioning

confidence: 99%

Knowing What a User Likes: A Design Science Approach to Interfaces that Automatically Adapt to Culture

Bernstein

2013

MISQ

163

“…However, adaptive systems in other disciplines have previously been demonstrated to improve the performance of users (see, e.g., [Greenberg and Witten 1985;Sears and Shneiderman 1994;Höök 1997;Gajos et al 2008;Findlater and McGrenere 2008;Findlater et al 2009]). Since cultural background affects the preference for more or less guidance, or more or less complex interfaces [Marcus and Gould 2001;Reinecke and Bernstein 2009], the same could be expected for culturally adaptive interfaces.…”

Section: Related Workmentioning

confidence: 99%

Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces

ACM Trans. Comput.-Hum. Interact.

Bernstein

2011

186

When we investigate the usability and aesthetics of user interfaces, we rarely take into account that what users perceive as beautiful and usable strongly depends on their cultural background. In this paper, we argue that it is not feasible to design one interface that appeals to all users of an increasingly global audience. Instead, we propose to design culturally adaptive systems, which automatically generate personalized interfaces that correspond to cultural preferences. In an evaluation of one such system, we demonstrate that a majority of international participants preferred their personalized versions over a non-adapted interface of the same web site. Results show that users were 22% faster using the culturally adapted interface, needed less clicks, and made fewer errors, in line with subjective results demonstrating that they found the adapted version significantly easier to use. Our findings show that interfaces that adapt to cultural preferences can immensely increase the user experience. When we investigate the usability and aesthetics of user interfaces, we rarely take into account that what users perceive as beautiful and usable strongly depends on their cultural background. In this paper, we argue that it is not feasible to design one interface that appeals to all users of an increasingly global audience. Instead, we propose to design culturally adaptive systems, which automatically generate personalized interfaces that correspond to cultural preferences. In an evaluation of one such system, we demonstrate that a majority of international participants preferred their personalized versions over a non-adapted interface of the same web site. Results show that users were 22% faster using the culturally adapted interface, needed less clicks, and made fewer errors, in line with subjective results demonstrating that they found the adapted version significantly easier to use. Our findings show that interfaces that adapt to cultural preferences can immensely increase the user experience.

“…Thus, data obtained from in situ observations of natural pointing interactions differs substantially from data collected in laboratory settings [2]. Yet, if lab-quality data could be collected in situ, it might enable measuring longitudinal fluctuations in a user's motor performance (e.g., due to medication, fatigue, or progression of a disease) [11], automatic detection of pointing problems and the need for assistive technology [10], automatic adaptation of user interfaces to the changes in the user's motor abilities [7], and more realistic assessments of novel input devices.…”

Section: Introductionmentioning

confidence: 99%

Accurate measurements of pointing performance from in situ observations

Gajos

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Herrmann

2012

We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.