Advertisement

Development of Quantitative Metrics to Support UI Designer Decision-Making in the Design Process

  • Young Sik Yoon
  • Wan Chul Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4550)

Abstract

The UI designer must be able to anticipate cognitive difficulties of users in the UI design process. However, the designer is likely to make erroneous judgments in the context of increasing functionality. Furthermore, time constraints in the development process exacerbate the design problem. There are various techniques to support the UI designer in the design process, including abstract design principles, specific design guidelines, design cases, design inspections, and design metrics. Metrics can summarize the status of a UI design solution more objectively and more accurately than human designers. This paper aims to develop quantitative metrics based on a unified framework for interaction design, which decomposes UI design problem into the four components: information architecture, task procedure, system dynamics, and physical interface. Three metrics were proposed to assist designer’s decision-making, including incongruity, complexity, and inefficiency. A case study shows that the proposed metrics can support the designer’s decision making in an efficient manner.

Keywords

Model-based UI Design Metrics Design Aids Usability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yoon, W.C.: Identifying, Organizing and Exploring Problem Space for Interaction Design. In: Proceedings of the 8th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Kassel, Germany, pp. 81–86 (2001)Google Scholar
  2. 2.
    Nielsen, J., Mack, R.L.: Usability Inspection Methods. John Wiley & Sons, Inc., West Sussex, England (1994)Google Scholar
  3. 3.
    Sears, A.: AIDE: A step toward metric-based interface development tools. In: Proceedings of the 8th Annual ACM Symposium on User Interface and Software Technology, Pittsburgh, Pennsylvania, United States, pp. 101–110 ( 1995)Google Scholar
  4. 4.
    ISO: ISO/IEC DIS 14598-1 Information Technology – Evaluation of Software Products – Part 1, General Guide (1996) Google Scholar
  5. 5.
    Yoon, W.C., Park, J.S.: A diagrammatic model for representing user’s interface knowledge of task procedures. In: Proceedings of Cognitive Systems Engineering in Process Control, Kyoto, Japan, pp. 276–285 (1996)Google Scholar
  6. 6.
    Kang, H.G., Seong, P.H.: An Information Theory-Based Approach for Quantitative Evaluation of User Interface Complexity. IEEE Trans. on Nuclear Science 45(6), 3165–3174 (1998)CrossRefGoogle Scholar
  7. 7.
    Hix, D., Hartson, H.R.: Developing User Interfaces – Ensuring Usability Through Product and Process. John Wiley & Sons, Inc., New York (1993)Google Scholar
  8. 8.
    Paterno, F.: Tools for Task Modeling: Where we are, Where we are headed. In: Proceedings of the 1st International Workshop on Task Models and Diagrams for User Interface Design, Bucharest, Romania, pp. 10–17 (2002)Google Scholar
  9. 9.
    Lee, D.S., Yoon, W.C.: Coupling structural and functional models for interaction design. Interacting with Computers 16, 133–161 (2004)CrossRefGoogle Scholar
  10. 10.
    Yoon, W.C.: Task-Interface Matching: How we may design user interfaces. In: Proceedings of the 15th Triennial Congress of the International Ergonomics Association, Seoul, Korea (2003)Google Scholar
  11. 11.
    Navrre, D., Palanque, P., Paterno, F., Santoro, C., Bastide, R.: A tool suite for the coevolutionary design of user interfaces. In: Proceedings of the 8th International Workshop on Design, Specification of Interactive Systems, Glasgow, Scotland, pp. 88–113 (2001)Google Scholar
  12. 12.
    Paterno, F.: Model-based Design and Evaluation of Interactive Applications. Springer, Heidelberg (1999)Google Scholar
  13. 13.
    Visser, W.: Use of episodic knowledge and information in design problem solving. In: Cross, N., Christiaans, H., Dorst, K. (eds.) Analysing Design Activity, pp. 271–289. Wiley, New York (1996)Google Scholar
  14. 14.
    Rouse, W.B., Rouse, S.H.: Measure of Complexity of Fault Diagnosis Tasks. IEEE Trans. On Systems, Man, and Cybernetics 9(11), 720–727 (1979)CrossRefGoogle Scholar
  15. 15.
    Payne, J.S., Green, T.R.G.: Task-Action Grammars: A model of the mental representation of task languages. Human-Computer Interaction 2, 93–133 (1986)CrossRefGoogle Scholar
  16. 16.
    Lee, D.S., Yoon, W.C, Choi, S.S.: An Entropy-Based Measure for Evaluating the Cognitive Complexity of User Interface. Korean Journal of The Science of Emotion and Sensibility 1(1), 213–221 (1998)Google Scholar
  17. 17.
    John, B.E., Kieras, D.E.: Using GOMS for user interface design and evaluation: Which technique? ACM Trans. on Computer-Human Interaction 3(4), 287–319 (1996)CrossRefGoogle Scholar
  18. 18.
    Wickens, C.D.: Engineering Psychology of Human Performance, HarperCollins Publiser Inc. (1992)Google Scholar
  19. 19.
    Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19, 255–259 (1998)zbMATHCrossRefGoogle Scholar
  20. 20.
    Hahn, U., Chater, N., Richardson, L.B.: Similarity as transformation. Cognition 87, 1–32 (2003)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Young Sik Yoon
    • 1
  • Wan Chul Yoon
    • 1
  1. 1.Department of Industrial Engineering, KAIST, 373-1, Guseong-dong, Yuseong-gu, TaejeonKorea

Personalised recommendations