Exploring the role of individual differences in information visualization

ABSTRACT

In this paper, we describe a user study aimed at evaluating the effectiveness of two different data visualization techniques developed for describing complex environmental changes in an interactive system designed to foster awareness in sustainable development. While several studies have compared alternative visualizations, the distinguishing feature of our research is that we try to understand whether individual user differences may be used as predictors of visualization effectiveness in choosing among alternative visualizations for a given task. We show that the cognitive ability known as perceptual speed can predict which one of our target visualizations is most effective for a given user. This result suggests that tailored visualization selection can be an effective way to improve user performance.

REFERENCES

• 1: R. A. Amar, J. Eagan and J. T. Stasko, Low-Level Components of Analytic Activity in Information Visualization., in: 16th IEEE Visualization Conference (VIS 2005), (2005).
• 2: M. Baldonado, A. Woodruff and A. Kuchinsky, Guidelines for Using Multiple Views in Information Visualization., in: Advanced Visual Interfaces 2000.
• 3: P. Brusilovsky, J. Ahn, T. Dumitriu and M. Yudelson, Adaptive Knowledge-Based Visualization for Accessing Educational Examples., in: Proceedings of Information Visualization, (2006).
• 4: W. S. Cleveland and R. Mcgill, Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 229 (1995) 828--833.
• 5: R. Ekstrom, J. French, H. Harman and D. Dermen, Manual from Kit of Factor-References Cognitive Tests. 1976, Educational Testing Service (1976): Princeton, NJ.
• 6: J. Mackinlay, Automating the Design of Graphical Presentations of Relational Information. Transactions on Graphics 5 (2) (1986) 110--141.
• 7: L. Nowell, R. Schulman and D. Hix, Graphical Encoding for Information Visualization: An Empirical Study, in: IEEE Symposium on Information Visualization (InfoVis'02), (2002) 53--81.
• 8: J. Tansey, J. Carmichael, R. Van Wynsberghe and J. Robinson, The Future Is Not What It Used to Be: Participatory Integrated Assessment in the Georgia Basin. Global Environmental Change: Human and Policy Dimensions 12 (2) (2002) 97--104.
• 9: M. Velez, D. Silver and M. Tremaine, Understanding Visualization through Spatial Ability Differences., in: Proceedings of Visualization 2005.
• 10: M. Williams and T. Munzner, Steerable, Progressive, Multidimensional Scaling, in: InfoVis 2004,
• 11: R. M. Felder and L. K. Silverman, "Learning and Teaching Styles in Engineering Education," Engr. Education, 78(7), 674--681 (1988),
• 12: Cacioppo, J. T., R. E. Petty, et al. The efficient Assessment of Need for Cognition. Journal of Personality Assessment 48(3): 306--307, 1984.
• 13: Chen C. Individual Differences in Spatial-Semantic Virtual Environments Journal of the American Society of Information Science, 51, 6, 2000
• 14: Dillon D. Spatial Semantics: How Users Derive Shape from Information Space. Journal of the American Society of Information Science, 51, 6, 2000
• 15: Allen B. Individual Differences and Cundrums of User-centered Design: Two Experiments. Journal of the American Society of Information Science, 51, 6, 2000

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.m Miscellaneous

Keywords:
evaluation of visualization techniques, individual differences