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.
Data were collected in a browser-based transcription task hosted on a university server. Our test supports the main mobile operating systems and browsers and was available globally on the Internet. Our participants volunteered via the public website typingtest.com by TypingMaster Inc., a private company offering typing testing and training.
PDF, 2.2 MB
How do People Type on Mobile Devices? Observations from a Study with 37,000 Volunteers.
In Proceedings of the ACM MobileHCI conference (MOBILEHCI’19).
@inproceedings{palin2019typing,
author = {Palin, Kseniia and Feit, Anna and Kim, Sunjun and Kristensson, Per Ola and Oulasvirta, Antti},
booktitle = {Proceedings of 21st International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI'19)},
title = {{How do People Type on Mobile Devices? Observations from a Study with 37,000 Volunteers.}},
year = {2019}
publisher = {ACM}
doi = {https://doi.org/10.475/123_4}
keywords = {mobile text entry, word prediction, auto-correct}
}
For questions and further information, please contact:
Antti Oulasvirta
Email:
antti.oulasvirta (at) aalto.fi
Acknowledgements: This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 637991) and the ERC Grant OPTINT (StG-2016-717054). Data collection was supported by Typing Master, Inc.