Abstract
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.
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BibTeX (Download)
@article{10.1145/3604276,
title = {Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates},
author = {Florian Lehmann and Itto Kornecki and Daniel Buschek and Anna Maria Feit},
url = {https://dl.acm.org/doi/abs/10.1145/3604276
https://osf.io/u9aej/},
doi = {10.1145/3604276},
year = {2023},
date = {2023-09-01},
urldate = {2023-09-01},
journal = {Proc. ACM Hum.-Comput. Interact.},
volume = {7},
number = {MHCI},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {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.},
keywords = {intelligent text entry methods, mobile text entry, text entry, typing, word prediction, word suggestion},
pubstate = {published},
tppubtype = {article}
}