Towards Location-Independent Eyes-Free Text Entry

We propose an interface for eyes-free text entry using an ambiguous technique and conduct a preliminary user study. We find that user are able to enter text at 19.09 words per minute (WPM) with a 2.08% character error rate (CER) after eight hours of practice. We explore ways to optimize the ambiguous groupings to reduce the number of disambiguation errors, both with and without familiarity constraints. We find that it is feasible to reduce the number of ambiguous groups from six to four. Finally, we explore a technique for presenting word suggestions to users using simultaneous audio feedback. We find that accuracy is quite poor when the words are played fully simultaneously, but improves when a slight delay is added before each voice

An Ambiguous Technique for Nonvisual Text Entry

Text entry is a common daily task for many people, but it can be a challenge for people with visual impairments when using virtual touchscreen keyboards that lack physical key boundaries. In this thesis, we investigate using a small number of gestures to select from groups of characters to remove most or all dependence on touch locations. We leverage a predictive language model to select the most likely characters from the selected groups once a user completes each word. Using a preliminary interface with six groups of characters based on a Qwerty keyboard, we find that users are able to enter text with no visual feedback at 19.1 words per minute (WPM) with a 2.1% character error rate (CER) after five hours of practice. We explore ways to optimize the ambiguous groups to reduce the number of disambiguation errors. We develop a novel interface named FlexType with four character groups instead of six in order to remove all remaining location dependence and enable one-handed input. We compare optimized groups with and without constraining the group assignments to alphabetical order in a user study. We find that users enter text with no visual feedback at 12.0 WPM with a 2.0% CER using the constrained groups after four hours of practice. There was no significant difference from the unconstrained groups. We improve FlexType based on user feedback and tune the recognition algorithm parameters based on the study data. We conduct an interview study with 12 blind users to assess the challenges they encounter while entering text and solicit feedback on FlexType, and we further incorporate this feedback into the interface. We evaluate the improved interface in a longitudinal study with 12 blind participants. On average, participants entered text at 8.2 words per minute using FlexType, 7.5 words per minute using a Qwerty keyboard with VoiceOver, and at 26.9 words per minute using Braille Screen Input

FlexType: Flexible Text Input with a Small Set of Input Gestures

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

Enhancing the composition task in text entry studies: Eliciting dificult text and improving error rate calculation

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 insufcient 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 difculty of their compositions based on simple instructions. While it took more time to compose difcult messages, they were longer, had more difcult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant\u27s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate

Statistical Keyboard Decoding

Text entry is a core task in our daily interaction with computers. Entering text using a keyboard remains the de facto standard due to its familiarity and efficiency. However, new interaction settings and devices make conventional text entry using a keyboard more challenging due to higher levels of uncertainty in detected user interaction events. For example, entering text on a smartwatch using a very small keyboard layout naturally results in less accurate touches than can be expected when entering text on a smartphone or physical keyboard. Fortunately, statistical keyboard decoding provides a technique for inferring the user\u27s intended text from their noisy input. The approach leverages Bayes\u27 Rule to help identify the most probable word given a model of the user\u27s uncertain touch interaction and known language regularities. This chapter provides an overview of statistical keyboard decoding and examines the various design parameters which are known to dictate its performance. Two illustrative case studies are also presented which demonstrate how statistical keyboard decoding can enable efficient text entry in challenging interaction settings

Modeling the Growth and Spread of Infectious Diseases to Teach Computational Thinking

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