Methodologies for the analysis of usage patterns in information visualization

ABSTRACT

In this position paper, we describe two methods for the analysis of sequences of interaction with information visualization tools -- log file analysis and thinking aloud. Such an analysis is valuable because it can help designers to understand cognition processes of the users and, as a consequence, to improve the design of information visualizations. In this context, we also discuss the issue of categorization of user activities. Categorization helps researchers to generalize results and compare different information visualization tools.

References

1. Boren, T. and Ramey, J. 2000. Thinking aloud: reconciling theory and practice. IEEE Trans Profl Commun (Sep. 2000), 261--278. DOI=10.1109/47.867942.
2. Carpendale, S. 2008. Evaluating information visualization. In A. Kerren, J. Stasko, J. Fekete, and C. North, editors, Information Visualization: Human-Centred Issues and Perspectives. Springer, Berlin, Heidelberg, New York, 19--25.
3. Ericsson, K. A. and Simon, H. A. 1993. Protocol analysis. Verbal report as data. Revised edition, MIT Press, Cambridge, Mass., London, England.
4. Friel, P., Curcio, F. R. and Bright, G. W. 2001. Making sense of graphs: Critical factors influencing comprehension and instructional implications. J Res Math Educ, 32, (March 2001) 124--158.
5. Ivory, M. Y. and Hearst, M. A. 2001. The state of the art in automating usability evaluation of user interfaces. ACM Comput Surv, 33, 4, 470--516.
6. Lazar, J., Feng, J. H. and Hochheiser, H. 2010. Research Methods in Human-Computer Interaction. Wiley, Chichester.
7. Liu, Z., Nersessian, N. J., and Stasko, J. 2008. Distributed cognition as theoretical framework for information visualization. IEEE Trans Vis Comput Graph 14, 6, 1173--1180, DOI=10.1109/TVGC.2008.121
8. Mirel, B. 2004. Interaction design for complex problem solving. Elsevier/Morgan Kauffmann: Amsterdam, Boston, Heidelberg.
9. Pike, W. A., Stasko, J., Chang, R. and O'Connel, T. A. 2009. The science of interaction. InfVis 8, 4, 263--274.
10. Pohl, M., Wiltner, S., Miksch, S., Aigner, W., Rind, A. 2012. Analysing interactivity in information visualisation. Künstl Intell, 2012, 26, 151--159. DOI=10.1007/s13218-012-0167-6
11. Preece, J., Rogers, Y., Sharp, H., Benyon, D., Holland, S., Carey, T. 1994. Human-Computer Interaction. Addison-Wesley: Wokingham, England, Reading Mass., Menlo Park, California.
12. Preece, J., Rogers, Y., Sharp, H. 2002. Interaction Design. John Wiley, New York
13. Riche, N. H. 2010. Beyond system logging: human logging for evaluating information visualization. In Proceedings of the BELIV 2012 Workshop, DOI=http://doi.acm.org/10.1145/2110192
14. Sarayiya, P., North, C., Duca, K. 2010. Comparing benchmark task and insight evaluation methods. In Proceedings of the BELIV 2012 Workshop, 55--62, DOI=http://doi.acm.org/10.1145/2110192
15. Ratwani, R. M., Trafton, J. G. Boehm-Davis, D. A. 2008. Thinking graphically, connecting vision and cognition during graph comprehension. J. Exp. Psychol. Appl. 14, 1, 36--49. DOI: 10.1037/1076-898X.14.1.36
16. Shrinivasan, Y. B., van Wijk, J. J. 2009. Supporting Exploration Awareness in Information Visualization. IEEE Comput Graph Appl 29(5), 34--43. DOI: 10.1109/MCG.2009.87
17. Tversky, B. 2007. Visuospatial reasoning. In Holyoak, K. J., Morrison, R. G. (eds.) The Cambridge handbook of thinking and reasoning. 2^nd reprint. Cambridge University Press, Cambridge, 209--240.
18. Yi, J. S., Kang, Y., Stasko, J. T. and Jacko, J. 2007. toward a deeper understanding of the role of interaction in information visualization. IEEE Trans Vis Comput Graph 13, 6, 1224--1231.

Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

    • HCI design and evaluation methods