In many situations, it may be impractical or impossible to enter text
by selecting precise locations on a physical or touchscreen keyboard.
We present an ambiguous keyboard with four character groups that
has potential applications for eyes-free text entry, as well as text entry
using a single switch or a brain-computer interface.We develop
a procedure for optimizing these character groupings based on a disambiguation
algorithm that leverages a long-span language model.
We produce both alphabetically-constrained and unconstrained
character groups in an offline optimization experiment and compare
them in a longitudinal user study. Our results did not show a
significant difference between the constrained and unconstrained
character groups after four hours of practice. As expected, participants
had significantly more errors with the unconstrained groups
in the first session, suggesting a higher barrier to learning the
technique.We therefore recommend the alphabetically-constrained
character groups, where participants were able to achieve an average
entry rate of 12.0 words per minute with a 2.03% character
error rate using a single hand and with no visual feedback.
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Enhancing the Composition Task in Text Entry Studies: Eliciting Difficult Text and Improving Error Rate Calculation
Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant’s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.
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- Award ID(s):
- 1909248
- NSF-PAR ID:
- 10237296
- Date Published:
- Journal Name:
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3411764.3445199
