Maximizing the guessability of symbolic input

Abstract

Guessability is essential for symbolic input, in which users enter gestures or keywords to indicate characters or commands, or rely on labels or icons to access features. We present a unified approach to both maximizing and evaluating the guessability of symbolic input. This approach can be used by anyone wishing to design a symbol set with high guessability, or to evaluate the guessability of an existing symbol set. We also present formulae for quantifying guessability and agreement among guesses. An example is offered in which the guessability of the EdgeWrite unistroke alphabet was improved by users from 51.0% to 80.1% without designer intervention. The original and improved alphabets were then tested for their immediate usability with the procedure used by MacKenzie and Zhang (1997). Users entered the original alphabet with 78.8% and 90.2% accuracy after 1 and 5 minutes of learning, respectively. The improved alphabet bettered this to 81.6% and 94.2%. These improved results were competitive with prior results for Graffiti, which were 81.8% and 95.8% for the same measures.

User-Defined Gestures for Augmented Reality

“…Is there a taxonomy that can be used to classify gestures in AR? Similar shortcomings were encountered in the fields of surface computing and motion gestures, where Wobbrock et al [9] and Ruiz et al [10] addressed absences of design insight by conducting guessability studies [11].…”

“…The technique is common in participatory design [17] and has been applied in a variety of research areas such as unistroke gestures [11], surface computing [9] and motion gesture for mobile interaction [10]. In AR, a Wizard of Oz study [15] for gestures and speech was conducted and aimed to capture the type of speech and gesture input that users would like to use in an object manipulation task.…”

“…To compute the degree of consensus among the designed gestures, an agreement score A was calculated using Equation 1 [11]: (1) where P t is the total number of gestures within the task, t, P s is a subset of P t containing similar gestures, and the range of A is [0, 1].…”

“…In addition to this, there were some tasks where there were two or more gestures commonly assigned by the participants. This non one-to-one mapping resulted in a consensus set of 44 gestures for a total of 40 tasks, which resulted in improved guessability [11].…”

Ability-Based Design

“…Trackballs are popular among users with SCI, and many users employ an on-screen point-and-click (or point-and-dwell) keyboard to enter text, which is tedious. To address this, we built Trackball EdgeWrite (Wobbrock and Myers 2006a, Wobbrock and Myers 2006c, Wobbrock and Myers 2007, which enables users with SCI to write gesturally with their trackballs by "pulsing" the ball in directions corresponding to strokes in the EdgeWrite alphabet (Wobbrock et al 2005), which was originally designed for mobile device users with tremor (Wobbrock et al 2003b). Trackball EdgeWrite maps users' ball motions to vectors corresponding to segments within EdgeWrite letters, and employs adaptive timeouts, slip detection, and word prediction and completion to improve users' accuracy and speed.…”