In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface. We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant difference between the constrained and unconstrained character groups after four hours of practice. As expected, participants had significantly more errors with the unconstrained groups in the first session, suggesting a higher barrier to learning the technique. We therefore recommend the alphabetically-constrained character groups, where participants were able to achieve an average entry rate of 12.0 words per minute with a 2.03% character error rate using a single hand and with no visual feedback.

Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant’s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.

Modeling is commonly employed in school settings to help students develop an understanding of biological systems [3]. By inspecting and modifying the inner workings of their models, students become familiar with causal factors and how they impact the properties of the model. We believe that allowing students to tinker with computational models involves developing the same skills used in computational thinking, such as abstraction, decomposition, analysis, automation, and generalization. In this poster, we discuss the design and implementation of a simulation that models the growth and spread of a hypothetical disease. The goal is to help middle school students develop computational thinking skills while learning how a virus spreads through the human population.

Virtual keyboard typing is typically aided by an auto-correct method that decodes a user's noisy taps into their intended text. This decoding process can reduce error rates and possibly increase entry rates by allowing users to type faster but less precisely. However, virtual keyboard decoders sometimes make mistakes that change a user's desired word into another. This is particularly problematic for challenging text such as proper names. We investigate whether users can guess words that are likely to cause auto-correct problems and whether users can adjust their behavior to assist the decoder. We conduct computational experiments to decide what predictions to offer in a virtual keyboard and design a smartwatch keyboard named VelociWatch. Novice users were able to use the features of VelociWatch to enter challenging text at 17 words-per-minute with a corrected error rate of 3%. Interestingly, they wrote slightly faster and just as accurately on a simpler keyboard with limited correction options. Our finding suggest users may be able to type difficult words on a smartwatch simply by tapping precisely without the use of auto-correct.

Mobile text entry has become an increasingly important part of many peoples' daily lives. While most input occurs through individual letters being tapped on a virtual QWERTY keyboard, this does not have to be the case. We explore how well users are able to learn an ambiguous keyboard that is modeled after a standard QWERTY layout but does not require users to tap specific keys. We show that this keyboard is a plausible text entry technique for users with little or no vision, with users achieving 19.09 Words per Minute (WPM) and 2.08% Character Error Rate after 8 hours of practice.

Entering text on non-desktop computing devices is often done via an onscreen virtual keyboard. Input on such keyboards normally consists of a sequence of noisy tap events that specify some amount of text, most commonly a single word. But is single word-at-a-time entry the best choice? This paper compares user performance and recognition accuracy of word-at-a-time, phrase-at-a-time, and sentence-at-a-time text entry on a smartwatch keyboard. We evaluate the impact of differing amounts of input in both text copy and free composition tasks. We found providing input of an entire sentence significantly improved entry rates from 26 wpm to 32 wpm while keeping character error rates below 4%. In offline experiments with more processing power and memory, sentence input was recognized with a much lower 2.0% error rate. Our findings suggest virtual keyboards can enhance performance by encouraging users to provide more input per recognition event.