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Typing Behavior is About More than Speed : Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates

Title data

Lehmann, Florian ; Kornecki, Itto ; Buschek, Daniel ; Feit, Anna Maria:
Typing Behavior is About More than Speed : Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates.
In: Proceedings of the ACM on Human-Computer Interaction. Vol. 7 (2023) Issue MHCI . - 229.
ISSN 2573-0142
DOI: https://doi.org/10.1145/3604276

Project information

Project title:
Project's official title
Project's id
AI Tools - Continuous Interaction with Computational Intelligence Tools
No information

Abstract in another language

Mobile word suggestions can slow down typing, yet are still widely used. To investigate the apparent benefits beyond speed, we analyzed typing behavior of 15,162 users of mobile devices. Controlling for natural typing speed (a confounding factor not considered by prior work), we statistically show that slower typists use suggestions more often but are slowed down by doing so. To better understand how these typists leverage suggestions — if not to improve their speed — we extract eight usage strategies, including completion, correction, and next-word prediction. We find that word characteristics, such as length or frequency, along with the strategy, are predictive of whether a user will select a suggestion. We show how to operationalize our findings by building and evaluating a predictive model of suggestion selection. Such a model could be used to augment existing suggestion algorithms to consider people's strategic use of word predictions beyond speed and keystroke savings.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: mobile text entry; intelligent text entry methods; word suggestion; typing; text entry; word prediction
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IX > Chair Applied Computer Science - Univ.-Prof. Dr. Daniel Buschek
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IX
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Sep 2023 11:04
Last Modified: 08 Feb 2024 13:02
URI: https://eref.uni-bayreuth.de/id/eprint/86872