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Improvements in Interface Design through Implicit Modeling

  • Patrick K. A. Wollner
  • Ian Hosking
  • Patrick M. Langdon
  • P. John Clarkson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8009)

Abstract

Touchscreen devices are often limited by the complexity of their user interface design. In the past, iterative design processes using representative user groups to test prototypes were the standard method for increasing the inclusivity of a given design, but cognitive modeling has potential to be an alternative to rigorous user testing. However, these modeling approaches currently have many limitations, some of which are based on the assumptions made in translating a User Interface (UI) into a definition file that cognitive modeling frameworks can process. This paper discusses these issues and postulates potential approaches to improvements to the translation procedure.

Keywords

inclusive design universal design cognitive modeling cognitive architectures 

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References

  1. 1.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111, 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    British Standards Institution: Managing Inclusive Design. Number BS 7000–6:2005. In: Design management systems, London (2005)Google Scholar
  3. 3.
    Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction. Erlbaum (1983)Google Scholar
  4. 4.
    Card, S.K., Moran, T.P., Newell, A.: The Keystroke-Level Model for User Performance Time with Interactive Systems. Commun. ACM 23(7), 396–410 (1980)CrossRefGoogle Scholar
  5. 5.
    Compeau, D.R., Higgins, C.A.: Application of Social Cognitive Theory to Training for Computer Skills. Information Systems Research 6(2), 118–143 (1995)CrossRefGoogle Scholar
  6. 6.
    Councill, I.G., Haynes, S.R., Ritter, F.E.: Explaining Soar: Analysis of Existing Tools and User Information Requirements. In: Proceedings of the Fifth International Conference on Cognitive Modeling (2003)Google Scholar
  7. 7.
    Dunlop, M.D., Crossan, A.: Predicitve Text Entry Methods for Mobile Phones. Personal Technologies 4(2-3) (2000)Google Scholar
  8. 8.
    Gregor, P., Dickinson, A.: Cognitive difficulties and access to information systems: an interaction design perspective. Universal Access in the Information Society 5(4), 393–400 (2006)CrossRefGoogle Scholar
  9. 9.
    Ham, D., Heo, J., Fossick, P., Wong, W., Park, S., Song, C., Bradley, M.: Conceptual framework and models for identifying and organizing usability impact factors of mobile phones. In: Proceedings of the 20th Conference of the Computer-Human Interaction Special Interest Group (CHISIG) of Australia on Computer-Human Interaction Design: Activities, Artefacts and Environments - OZCHI 2006, Sydney, Australia, p. 261 (2006)Google Scholar
  10. 10.
    How, Y., Kan, M.Y.: Optimizing Predicitive Text Entry for Short Message Service on Mobile Phones. In: HCII 2005 (2005)Google Scholar
  11. 11.
    John, B.E.: Why GOMS? Interactions 2(4), 80–89 (1995)CrossRefGoogle Scholar
  12. 12.
    John, B.E., Prevas, K., Salvucci, D.D., Koedinger, K.: Predictive human performance modeling made easy. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2004, pp. 455–462. ACM, New York (2004)CrossRefGoogle Scholar
  13. 13.
    Kieras, D.E.: Using the Keystroke-Level Model to estimate execution times. University of Michigan (2001)Google Scholar
  14. 14.
    Langdon, P., Lewis, T., Clarkson, P.J.: The effects of prior experience on the use of consumer products. Universal Access in the Information Society 6(2), 179–191 (2007)CrossRefGoogle Scholar
  15. 15.
    Office for National Statistics: Statistical bulletin: Internet Access - Households and Individuals (2012), http://www.ons.gov.uk/ons/rel/rdit2/internet-access---households-and-individuals/2012/stb-internet-access--households-and-individuals--2012.html
  16. 16.
    Osterloh, J.P., Feil, R., Ludtke, A., Gonzalez-Calleros, J.: Automated UI Evaluation based on a Cognitive Architecture and UsiXML, pp. 1–9 (2011)Google Scholar
  17. 17.
    Pavlovych, A., Stürzlinger, W.: Model for Non-expert Text Entry Speed on 12-button Phone Keypads. In: CHI 2004, pp. 351–358. ACM Press (2004)Google Scholar
  18. 18.
    Persad, U., Langdon, P., Clarkson, P.J.: Characterising user capabilities to support inclusive design evaluation. Universal Access in the Information Society 6(2), 119–135 (2007)CrossRefGoogle Scholar
  19. 19.
    Ritter, F.E.: Some Frontiers of Cognitive Modeling: A Modest Research Agenda Exploring Emotions and Usability (2008)Google Scholar
  20. 20.
    Salvucci, D.D., Lee, F.J.: Simple Cognitive Modeling in a Complex Cognitive Architecture. In: Proceedings of CHI 2003, Human Factors in Computing Systems, pp. 265–272. ACM Press (2003)Google Scholar
  21. 21.
    St Amant, R., Ritter, F.E.: Automated GOMS-to-ACT-R Model Generation. In: International Conference on Cognitive Modeling, Lea, Mahwah, NJ, pp. 28–34 (2004)Google Scholar
  22. 22.
    Thimbleby, H., Gow, J.: Applying Graph Theory to Interaction Design. In: Gulliksen, J., Harning, M.B., Palanque, P., van der Veer, G.C., Wesson, J. (eds.) EIS 2007. LNCS, vol. 4940, pp. 501–519. Springer, Heidelberg (2008)Google Scholar
  23. 23.
    Wollner, P.K.A., Goldhaber, T.S., Mieczakowski, A., Langdon, P., Clarkson, P.J.: Evaluation of setup procedures on mobile devices based on users’ prior experience. In: NordDesign 2012 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Patrick K. A. Wollner
    • 1
  • Ian Hosking
    • 1
  • Patrick M. Langdon
    • 1
  • P. John Clarkson
    • 1
  1. 1.Engineering Design Centre, Department of EngineeringUniversity of CambridgeUK

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