research-article

The Effects of Predictive Features of Mobile Keyboards on Text Entry Speed and Errors

Authors:

Wolfgang Stuerzlinger,

Felix PutzeAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue ISS

Article No.: 183, Pages 1 - 16

https://doi.org/10.1145/3427311

Published: 04 November 2020 Publication History

Abstract

Mobile users rely on typing assistant mechanisms such as prediction and autocorrect. Previous studies on mobile keyboards showed decreased performance for heavy use of word prediction, which identifies a need for more research to better understand the effectiveness of predictive features for different users. Our work aims at such a better understanding of user interaction with autocorrections and the prediction panel while entering text, in particular when these approaches fail. We present a crowd-sourced mobile text entry study with 170 participants. Our mobile web application simulates autocorrection and word prediction to capture user behaviours around these features. We found that using word prediction saves an average of 3.43 characters per phrase but also adds an average of two seconds compared to actually typing the word, resulting in a negative effect on text entry speed. We also identified that the time to fix wrong autocorrections is on average 5.5 seconds but that autocorrection does not have a significant effect on typing speed.

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Cited By

Lehmann FKornecki IBuschek DFeit A(2023)Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing RatesProceedings of the ACM on Human-Computer Interaction10.1145/36042767:MHCI(1-26)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604276
Komninos A(2022)The Value of Open Data in HCI: A Case Report from Mobile Text Entry ResearchMultimodal Technologies and Interaction10.3390/mti60900716:9(71)Online publication date: 23-Aug-2022
https://doi.org/10.3390/mti6090071
Lu FXu Y(2022)Exploring Spatial UI Transition Mechanisms with Head-Worn Augmented RealityProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517723(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517723
Show More Cited By

Index Terms

The Effects of Predictive Features of Mobile Keyboards on Text Entry Speed and Errors
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Keyboards

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue ISS

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November 2020

488 pages

EISSN:2573-0142

DOI:10.1145/3433930

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Apple Inc.

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Published: 04 November 2020

Published in PACMHCI Volume 4, Issue ISS

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Cited By

Lehmann FKornecki IBuschek DFeit A(2023)Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing RatesProceedings of the ACM on Human-Computer Interaction10.1145/36042767:MHCI(1-26)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604276
Komninos A(2022)The Value of Open Data in HCI: A Case Report from Mobile Text Entry ResearchMultimodal Technologies and Interaction10.3390/mti60900716:9(71)Online publication date: 23-Aug-2022
https://doi.org/10.3390/mti6090071
Lu FXu Y(2022)Exploring Spatial UI Transition Mechanisms with Head-Worn Augmented RealityProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517723(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517723
Kristensson PMüllners TKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Design and Analysis of Intelligent Text Entry Systems with Function Structure Models and Envelope AnalysisProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445566(1-12)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445566

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