Understanding the Utility of Rationale in a Mixed-Initiative System for GUI Customization

  • Andrea Bunt
  • Joanna McGrenere
  • Cristina Conati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

In this paper, we investigate the utility of providing users with the system’s rationale in a mixed-initiative system for GUI customization. An evaluation comparing a version of the system with and without the rationale suggested that rationale is wanted by many users, leading to increased trust, understandability and predictability, but that not all users want or need the information.

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References

  1. 1.
    Bunt, A., Conati, C., McGrenere, J.: What Role Can Adaptive Support Play in an Adaptable System? In: Proc. of IUI, pp. 117–124 (2004)Google Scholar
  2. 2.
    Bunt, A., Conati, C., McGrenere, J.: Supporting Interface Customization Using a Mixed-Initiative Approach. In: Proc. of IUI, pp. 92–101 (2007)Google Scholar
  3. 3.
    Card, S.K., Newell, A., Moran, T.P.: The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, Inc, Mahwah, NJ (1983)Google Scholar
  4. 4.
    Czarkowski, M., Kay, J.: How to Give the User a Sense of Control over the Personalization of Adaptive Hypertext? In: Proc. of Adaptive Hypermedia and Adaptive Web-Based Systems (in conjunction with UM 2003), pp.121–131 (2003)Google Scholar
  5. 5.
    Debevc, M., Meyer, B., Donlagic, D., Svecko, R.: Design and Evaluation of an Adaptive Icon Toolbar. User Modeling and User-Adapted Interaction 6(1), 1–21 (1996)CrossRefGoogle Scholar
  6. 6.
    Gajos, K., Weld, D.S.: Supple: Automatically Generating User Interfaces. In: Proc. of IUI, pp. 93–100 (2004)Google Scholar
  7. 7.
    Greenberg, S., Witten, I.H.: How Users Repeat Their Actions on Computers: Principles for Design of History Mechanisms. In: Proc. of CHI, pp. 171–178 (1988)Google Scholar
  8. 8.
    Herlocker, J., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Proc. of CSCW, pp. 241–250 (2000)Google Scholar
  9. 9.
    Hook, K.: Steps to Take before Intelligent User Interfaces Become Real. Interacting with Computers 12, 409–426 (2000)CrossRefGoogle Scholar
  10. 10.
    Horvitz, E.: Principles of Mixed-Initiative User Interfaces. In: Proc. of CHI, pp. 159–166 (1999)Google Scholar
  11. 11.
    Horvitz, E., Breese, J., Henrion, M.: Decision Theory in Expert Systems and Artificial Intelligence. Journal of Approximate Reasoning 2, 247–302 (1988)CrossRefGoogle Scholar
  12. 12.
    Horvitz, E., Herckerman, D., Hovel, D., Rommelse, R.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proc. of UAI, pp. 256–265 (1998)Google Scholar
  13. 13.
    Mackay, W.E.: Triggers and Barriers to Customizing Software. In: Proc. of CHI, pp. 153–160 (1991)Google Scholar
  14. 14.
    McGrenere, J., Baecker, R.M., Booth, K.S.: An Evaluation of a Multiple Interface Design Solution for Bloated Software. In: Proc. of CHI, pp. 163–170 (2002)Google Scholar
  15. 15.
    Oppermann, R.: Adaptively Supported Adaptability. International Journal of Human-Computer Studies 40, 455–472 (1994)CrossRefGoogle Scholar
  16. 16.
    Zapata-Rivera, J.D., Greer, J.E.: Interacting with Inspectable Bayesian Student Models. International Journal of AI in Education 14, 127–163 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Andrea Bunt
    • 1
  • Joanna McGrenere
    • 1
  • Cristina Conati
    • 1
  1. 1.Computer Science Department, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4 

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