Performance comparisons of phrase sets and presentation styles for text entry evaluations

Kristensson, Per Ola; Vertanen, Keith

doi:10.1145/2166966.2166972

Cited by 91 publications

(34 citation statements)

References 9 publications

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“…This set consists of sentences from genuine mobile emails that have been validated to be memorable. It has also been shown [4] that this set of sentences results in comparable performances to the earlier and less externally valid phrase set by MacKenzie and Soukoreff [7].…”

Section: Methodssupporting

confidence: 52%

An evaluation of Dasher with a high-performance language model as a gaze communication method

Rough

Vertanen

Kristensson

2014

Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces

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Dasher is a promising fast assistive gaze communication method. However, previous evaluations of Dasher have been inconclusive. Either the studies have been too short, involved too few participants, suffered from sampling bias, lacked a control condition, used an inappropriate language model, or a combination of the above. To rectify this, we report results from two new evaluations of Dasher carried out using a Tobii P10 assistive eye-tracker machine. We also present a method of modifying Dasher so that it can use a state-of-the-art long-span statistical language model. Our experimental results show that compared to a baseline eye-typing method, Dasher resulted in significantly faster entry rates (12.6 wpm versus 6.0 wpm in Experiment 1, and 14.2 wpm versus 7.0 wpm in Experiment 2). These faster entry rates were possible while maintaining error rates comparable to the baseline eye-typing method. Participants' perceived physical demand, mental demand, effort and frustration were all significantly lower for Dasher. Finally, participants significantly rated Dasher as being more likeable, requiring less concentration and being more fun.

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Section: Methodssupporting

confidence: 52%

An evaluation of Dasher with a high-performance language model as a gaze communication method

Rough

Vertanen

Kristensson

2014

Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces

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“…Before each phrase participants were asked to put their hands by their side, in order to mimic aperiodic interaction with the peripheral from VR. Based on [11], we used the MacKenzie 500 phrase set, with phrases chosen at random and presented at the top of the display, with the bottom of the display used for on-screen feedback of typing. Baseline typing was captured first, the remaining conditions were counterbalanced.…”

Section: Designmentioning

confidence: 99%

A Dose of Reality

McGill

Boland

Murray-Smith

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

174

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We identify usability challenges facing consumers adopting Virtual Reality (VR) head-mounted displays (HMDs) in a survey of 108 VR HMD users. Users reported significant issues in interacting with, and being aware of their real-world context when using a HMD. Building upon existing work on blending real and virtual environments, we performed three design studies to address these usability concerns. In a typing study, we show that augmenting VR with a view of reality significantly corrected the performance impairment of typing in VR. We then investigated how much reality should be incorporated and when, so as to preserve users' sense of presence in VR. For interaction with objects and peripherals, we found that selectively presenting reality as users engaged with it was optimal in terms of performance and users' sense of presence. Finally, we investigated how this selective, engagement-dependent approach could be applied in social environments, to support the user's awareness of the proximity and presence of others.

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“…We used phrases from the Enron Mobile Email dataset [36] as stimuli. This phrase set consists of phrases drawn from genuine mobile emails and has been shown to result in similar text entry performance as the MacKenzie and Soukoreff [22] phrase set [18]. We filtered out any phrases containing characters not on our keyboard, and then removed sentences with fewer than 4 or more than 10 words.…”

Section: Study 1: Gp and Correction Strategiesmentioning

confidence: 99%

Uncertain text entry on mobile devices

Weir

Pohl²,

Rogers

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7% over a baseline method, and by up to 1.3% over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate.

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Performance comparisons of phrase sets and presentation styles for text entry evaluations

Cited by 91 publications

References 9 publications

An evaluation of Dasher with a high-performance language model as a gaze communication method

An evaluation of Dasher with a high-performance language model as a gaze communication method

A Dose of Reality

Uncertain text entry on mobile devices

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