Gathering text entry metrics on android devices

Authors:
Steven J. Castellucci;I. Scott MacKenzie
Affiliations:
York University, Toronto, ON, Canada;York University, Toronto, ON, Canada
Venue:
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Year:
2011

Citing 4
Cited 7

Phrase sets for evaluating text entry techniques

CHI '03 Extended Abstracts on Human Factors in Computing Systems
Comparing the immediate usability of graffiti 2 and virtual keyboard

CHI '04 Extended Abstracts on Human Factors in Computing Systems
Recent developments in text-entry error rate measurement

CHI '04 Extended Abstracts on Human Factors in Computing Systems
Analyzing the input stream for character- level errors in unconstrained text entry evaluations

ACM Transactions on Computer-Human Interaction (TOCHI)

Revisiting storage for smartphones

FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
PinyinPie: a pie menu augmented soft keyboard for chinese pinyin input methods

MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
Revisiting storage for smartphones

ACM Transactions on Storage (TOS)
Freehand gestural text entry for interactive TV

Proceedings of the 11th european conference on Interactive TV and video
Improving two-thumb text entry on touchscreen devices

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring the use of speech input by blind people on mobile devices

Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility
Design and evaluation of a predictive model for smartphone selection

DUXU'13 Proceedings of the Second international conference on Design, User Experience, and Usability: web, mobile, and product design - Volume Part IV

Quantified Score

Hi-index	0.00

Visualization

Abstract

We developed an application to gather text entry speed and accuracy metrics on Android devices. This paper details the features of the application and describes a pilot study to demonstrate its utility. We evaluated and compared three mobile text entry methods: QWERTY typing, handwriting recognition, and shape writing recognition. Handwriting was the slowest and least accurate technique. QWERTY was faster than shape writing, but we found no significant difference in accuracy between the two techniques.