How do people type on mobile devices? Observations from a study with 37,000 volunteers

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How do people type on mobile devices? Observations from a study with 37,000 volunteers. / Palin, Kseniia; Feit, Anna Maria; Kim, Sunjun; Kristensson, Per Ola; Oulasvirta, Antti.

Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019. ACM, 2019. a9.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Palin, K, Feit, AM, Kim, S, Kristensson, PO & Oulasvirta, A 2019, How do people type on mobile devices? Observations from a study with 37,000 volunteers. in Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019., a9, ACM, International Conference on Human-Computer Interaction with Mobile Devices and Services, Taipei, Taiwan, Republic of China, 01/10/2019. https://doi.org/10.1145/3338286.3340120

APA

Palin, K., Feit, A. M., Kim, S., Kristensson, P. O., & Oulasvirta, A. (2019). How do people type on mobile devices? Observations from a study with 37,000 volunteers. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019 [a9] ACM. https://doi.org/10.1145/3338286.3340120

Vancouver

Palin K, Feit AM, Kim S, Kristensson PO, Oulasvirta A. How do people type on mobile devices? Observations from a study with 37,000 volunteers. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019. ACM. 2019. a9 https://doi.org/10.1145/3338286.3340120

Author

Palin, Kseniia ; Feit, Anna Maria ; Kim, Sunjun ; Kristensson, Per Ola ; Oulasvirta, Antti. / How do people type on mobile devices? Observations from a study with 37,000 volunteers. Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019. ACM, 2019.

Bibtex - Download

@inproceedings{fb3c6bd479984ea6a83f792059aa19aa,
title = "How do people type on mobile devices? Observations from a study with 37,000 volunteers",
abstract = "This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3{\%} uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.",
author = "Kseniia Palin and Feit, {Anna Maria} and Sunjun Kim and Kristensson, {Per Ola} and Antti Oulasvirta",
note = "| openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/H2020/717054/EU//OPTINT",
year = "2019",
month = "10",
day = "1",
doi = "10.1145/3338286.3340120",
language = "English",
booktitle = "Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019",
publisher = "ACM",

}

RIS - Download

TY - GEN

T1 - How do people type on mobile devices? Observations from a study with 37,000 volunteers

AU - Palin, Kseniia

AU - Feit, Anna Maria

AU - Kim, Sunjun

AU - Kristensson, Per Ola

AU - Oulasvirta, Antti

N1 - | openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/H2020/717054/EU//OPTINT

PY - 2019/10/1

Y1 - 2019/10/1

N2 - This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.

AB - This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.

UR - http://www.scopus.com/inward/record.url?scp=85073536418&partnerID=8YFLogxK

U2 - 10.1145/3338286.3340120

DO - 10.1145/3338286.3340120

M3 - Conference contribution

BT - Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019

PB - ACM

ER -

ID: 38210168