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Christina L. James

dblp:45/6754 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › text entry
predictive text entry		 0.012001
Text input for mobile devices: comparing model prediction to actual performance · CHI 2001
Interaction techniques and input
text entry		 0.012001
Text input for mobile devices: comparing model prediction to actual performance · CHI 2001
Interaction techniques and input
mobile interaction		 0.012001
Text input for mobile devices: comparing model prediction to actual performance · CHI 2001

Methods — techniques the papers use, named apart from their topics

mathematical modeling of interaction performance · 0.0
YearPublicationVenuePosition
2001 Text input for mobile devices: comparing model prediction to actual performance
abstract
A study was conducted to obtain performance data for entering text on a mobile phone in order to compare it to performance predictions based on two different mathematical models. Speed data was obtained for two text input methods, T9 Text Input and Multi-tap. While the direction of the results was the same for both the performance data and both model predicitons (with predictive text entry being faster than Multi-tap text entry), the results for all three differed in magnitude. Suggestions for this discrepancy are provided. In addition, in order to help shape future models, additional results are presented for both input methods to show how both accuracy and speed performance varies based on user experience and text subject matter.
Christina L. James, Kelly M. Reishcel
 CHI1