In search of effective text input interfaces for off the desktop computing

Zhai, Shumin; Kristensson, Per Ola; Smith, Barton A.

doi:10.1016/j.intcom.2003.12.007

Cited by 54 publications

(41 citation statements)

References 43 publications

(66 reference statements)

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“…Mobile text entry has been a topic of intense research for over two decades (see [6,12,13,19,20,27] for surveys and overviews). Text entry on touchscreen mobile devices is typically carried out using one of two intelligent text entry methods [12].…”

Section: Introductionmentioning

confidence: 99%

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Reyal

Zhai

Kristensson

2015

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

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We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0% to 3.6% character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-ofvocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations.

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Section: Introductionmentioning

confidence: 99%

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Reyal

Zhai

Kristensson

2015

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

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“…For instance, mobile text entry is different from desktop text entry [22,30], and typing on a tactile keyboard requires little or no visual attention, whereas text entry on a touch surface requires visual attention. Thus, text entry in non-desktop settings presents new challenges and requires new methods [39]. The present paper is motivated by a need to support text entry in one such setting, users working with a large high-resolution display.…”

Section: Introductionmentioning

confidence: 99%

Selection-Based Mid-Air Text Entry on Large Displays

Markussen

Jakobsen

Hornbæk

2013

Lecture Notes in Computer Science

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Abstract. Most text entry methods require users to have physical devices within reach. In many contexts of use, such as around large displays where users need to move freely, device-dependent methods are ill suited. We explore how selection-based text entry methods may be adapted for use in mid-air. Initially, we analyze the design space for text entry in mid-air, focusing on singlecharacter input with one hand. We propose three text entry methods: H4 MidAir (an adaptation of a game controller-based method by MacKenzie et al.[21]), MultiTap (a mid-air variant of a mobile phone text entry method), and Projected QWERTY (a mid-air variant of the QWERTY keyboard). After six sessions, participants reached an average of 13.2 words per minute (WPM) with the most successful method, Projected QWERTY. Users rated this method highest on satisfaction and it resulted in the least physical movement.

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“…Many of these devices are communicators that would benefit from rapid and accurate text entry for the sending of information from one party to another [Zhai et al 2005]. For example, mobile phones are used to send more than 15 billion SMS messages per month in Europe [GSM World 2004].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the input stream for character- level errors in unconstrained text entry evaluations

Wobbrock

Myers

2006

ACM Trans. Comput.-Hum. Interact.

171

106

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Recent improvements in text entry error rate measurement have enabled the running of text entry experiments in which subjects are free to correct errors (or not) as they transcribe a presented string. In these "unconstrained" experiments, it is no longer necessary to force subjects to unnaturally maintain synchronicity with presented text for the sake of performing overall error rate calculations. However, the calculation of character-level error rates, which can be trivial in artificially constrained evaluations, is far more complicated in unconstrained text entry evaluations because it is difficult to infer a subject's intention at every character. For this reason, prior characterlevel error analyses for unconstrained experiments have only compared presented and transcribed strings, not input streams. But input streams are rich sources of character-level error information, since they contain all of the text entered (and erased) by a subject. The current work presents an algorithm for the automated analysis of character-level errors in input streams for unconstrained text entry evaluations. It also presents new character-level metrics that can aid method designers in refining text entry methods. To exercise these metrics, we perform two analyses on data from an actual text entry experiment. One analysis, available from the prior work, uses only presented and transcribed strings. The other analysis uses input streams, as described in the current work. The results confirm that input stream error analysis yields richer information for the same empirical data. To facilitate the use of these new analyses, we offer pseudocode and downloadable software for performing unconstrained text entry experiments and analyzing data.

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In search of effective text input interfaces for off the desktop computing

Cited by 54 publications

References 43 publications

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Performance and User Experience of Touchscreen and Gesture Keyboards in a Lab Setting and in the Wild

Selection-Based Mid-Air Text Entry on Large Displays

Analyzing the input stream for character- level errors in unconstrained text entry evaluations

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