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Maximizing the guessability of symbolic input

Published:02 April 2005Publication History

ABSTRACT

Guessability is essential for symbolic input, in which users enter gestures or keywords to indicate characters or commands, or rely on labels or icons to access features. We present a unified approach to both maximizing and evaluating the guessability of symbolic input. This approach can be used by anyone wishing to design a symbol set with high guessability, or to evaluate the guessability of an existing symbol set. We also present formulae for quantifying guessability and agreement among guesses. An example is offered in which the guessability of the EdgeWrite unistroke alphabet was improved by users from 51.0% to 80.1% without designer intervention. The original and improved alphabets were then tested for their immediate usability with the procedure used by MacKenzie and Zhang (1997). Users entered the original alphabet with 78.8% and 90.2% accuracy after 1 and 5 minutes of learning, respectively. The improved alphabet bettered this to 81.6% and 94.2%. These improved results were competitive with prior results for Graffiti, which were 81.8% and 95.8% for the same measures.

References

  1. Brinck, T., Gergle, D. and Wood, S.D. (2001) Usability for the Web. San Francisco: Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Fleetwood, M.D., Byrne, M.D., Centgraf, P., Dudziak, K.Q., Lin, B. and Mogilev, D. (2002) An evaluation of text-entry in Palm OS-Graffiti and the virtual keyboard. In Proc. HFES 2002. Human Factors and Ergonomics Society, pp. 617--621.Google ScholarGoogle ScholarCross RefCross Ref
  3. Furnas, G. W., Landauer, T. K., Gomez, L. M. and Dumais, S. T. (1984) Statistical semantics: Analysis of the potential performance of keyword information systems. In Human Factors in Computer Systems, J.C. Thomas and M.L. Schneider (eds). Norwood, New Jersey: Ablex, pp. 187--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Furnas, G. W., Landauer, T. K., Gomez, L. M. and Dumais, S. T. (1987) The vocabulary problem in human-system communication. Communications of the ACM 30 (11), pp. 964--971. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Good, M. D., Whiteside, J. A., Wixon, D. R. and Jones, S. J. (1984) Building a user-derived interface. Communications of the ACM 27 (10), pp. 1032--1043. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Költringer, T. and Grechenig, T. (2004) Comparing the immediate usability of Graffiti 2 and virtual keyboard. In Proc. CHI 2004. ACM Press, pp. 1175--1178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. MacKenzie, I.S. and Zhang, S.X. (1997) The immediate usability of Graffiti. In Proc. Graphics Interface 1997. Canadian Information Processing Society, pp. 129--137. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Wiedenbeck, S. (1999) The use of icons and labels in an end user application program: An empirical study of learning and retention. Behavior and Information Technology 18 (2), pp. 68--82.Google ScholarGoogle ScholarCross RefCross Ref
  9. Wobbrock, J.O., Myers, B.A. and Kembel, J.A. (2003) EdgeWrite: A stylus-based text entry method designed for high accuracy and stability of motion. In Proc. UIST 2003. ACM Press, pp. 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          CHI EA '05: CHI '05 Extended Abstracts on Human Factors in Computing Systems
          April 2005
          1358 pages
          ISBN:1595930027
          DOI:10.1145/1056808

          Copyright © 2005 ACM

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          New York, NY, United States

          Publication History

          • Published: 2 April 2005

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