ABSTRACT
There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.
- Amar, R. A., Eagan J., & Stasko, J. T. Low-Level Components of Analytic Activity in Information Visualization. 16th IEEE Info. Vis. Conf., 15--21, 2005. Google ScholarDigital Library
- Bartram, L., Ware, C., & Calvert, T. Moticons: detection, distraction and task. Int. J. Hum.-Comput. Stud. 58(5), 515--545, 2003. Google ScholarDigital Library
- Bertin, J. Semiology of Graphics. University of Wisconsin Press, 1983. Google ScholarDigital Library
- Carenini, G., Loyd, J. Valuecharts: Analyzing Linear Models Expressing Preferences and Evaluations. In Proc. of the working conf. on Advanced visual interfaces, 150--157, 200 Google ScholarDigital Library
- Carenini G. & Rizoli L. A Multimedia Interface for Facilitating Comparisons of Opinions. Proc. 13th Int. Conf. on Intelligent User Interfaces, IUI 2009, 325--334. Google ScholarDigital Library
- Conati, C. & Maclaren, H. Exploring the Role of Individual Differences in Information Visualization. In Proc. Conf. on Advanced Vis. Interf., 199--206, 2008. Google ScholarDigital Library
- Conati, C., Hoque, E., Toker, D., & Steichen, B. When to Adapt: Detecting User's Confusion During Visualization Processing. Proc. 1st Int. Workshop on User-Adaptive Inf. Vis. (WUAV 2013), in conj. with UMAP 2013.Google Scholar
- D3 javascript library. http:// http://d3js.org/.Google Scholar
- Ekstrom, R., French, J., Harman, H. & Dermen, D., Manual from Kit of Factor-References Cognitive Tests. Educational Testing Service, Princeton, NJ, 1976.Google Scholar
- Elzer, S., Carberry, S., & Zukerman, I. The automated understanding of simple bar charts. Artificial Intelligence Journal 175(2), 526--555, 2011. Google ScholarDigital Library
- Erdfelder, E., Faul, F., & Buchner, A. GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28, 1--11, 1996.Google Scholar
- Few, S. Now You See It: Simple Visualization Techniques for Quantitative Analysis, First Edition. Analytics Press, 2009. Google ScholarDigital Library
- Field, A., & Hole, G., How to Design and Report Experiments. Sage Publications, London, 2003.Google Scholar
- Flatla, D. R., Gutwin, C. SSMRecolor: Improving Recoloring Tools with Situation-Specific Models of Color Differentiation. Proc. of Human factors in computing systems, CHI 2012, 2297--2306. Google ScholarDigital Library
- Fukuda, K., & Vogel, E. K. Human variation in overriding attentional capture. J. of Neurosc., 8726--8733, 2009.Google Scholar
- Gotz D., & Wen, Z.. Behavior Driven Visualization Recommendation. Proc. Conf. on Intelligent User Interfaces, IUI 2009, 315--324. Google ScholarDigital Library
- Gratzl, S., Lex, A., Gehlenborg, N., Pfister, H., & Streit, M. LineUp: Visual Analysis of Multi-Attribute Rankings. Visualiz. & Comp. Graph., 2277--2286, 2013. Google ScholarDigital Library
- Grawemeyer, B. Evaluation of ERST - an external representation selection tutor. Proc. Conf. on Diagrammatic Represent. and Inference, 154--167, 2006. Google ScholarDigital Library
- Green, T. M. & Fisher, B. Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. In IEEE Visual Analytics Sc. and Technology, 203--210, 2010.Google Scholar
- Harrower, M. & Brewer C. Colorbrewer.org: an online tool for selecting colour schemes for maps. The Cartographic Journal 40.1, 27--37, 2003.Google ScholarCross Ref
- Heer, J., Bostock, M., Ogievetsky, V. A Tour through the Visualization Zoo. Communications of the ACM, 53(6), 59--67, 2010. Google ScholarDigital Library
- Jameson, A. "Adaptive Interfaces and Agents" in Human-Computer Interface Handbook, eds J. A. Jacko and A. Sears, 305--330, 2003. Google ScholarDigital Library
- Kaptein, M. C., Nass, C., & Markopoulos, P. Powerful and Consistent Analysis of Likert-Type Rating Scales. Proc. Conf. on Human factors in Comp. Sys., CHI 2010, 2391--2394. Google ScholarDigital Library
- Kong, N., Agrawala, M. Graphical Overlays: Using Layered Elements to Aid Chart Reading. Visualization and Computer Graphics, 2631--2638, 2012.Google Scholar
- Kosslyn, S. M. Elements of Graph Design. W. H. Freeman and Company, 1994.Google Scholar
- Mittal, V. O. Visual Prompts and Graphical Design: A Framework for Exploring the Design Space of 2-D Charts and Graphs. Proc. AAAI/IAAI, 57--63, 1997. Google ScholarDigital Library
- Muir, M. & Conati, C.: An Analysis of Attention to Student - Adaptive Hints in an Educational Game. Proc. Intelligent Tutoring Systems, 112--122, 2012. Google ScholarDigital Library
- Palmer, E. M., Horowitz, T. S., Torralba A., Wolfe, J. What Are the Shapes of Response Time Distributions in Visual Search? J. of Exp. Psy.: Human Percep. and Perf., 37(1), 57--71, 2011.Google Scholar
- Rotter, Julian B. Generalized Expectancies for Internal Versus External Control of Reinforcement. Psych. Monographs: General and Applied 80.1, 1--28, 1966.Google Scholar
- Skytree Adviser. http://www.skytree.net/products-services/adviser-beta/faq/.Google Scholar
- Steichen, B., Carenini, G., Conati, C. User-Adaptive Information Visualization - Using eye gaze data to infer visualization tasks and user cognitive abilities. Proc. Conf. on Intelligent User Interfaces, IUI 2013, 317--328. Google ScholarDigital Library
- Toker, D., Conati, B., Steichen, Carenini, G. Individual User Characteristics and Information Visualization: Connecting the Dots through Eye Tracking. Proc. Conf. on Human Factors in Comp. Sys., CHI 2013, 295--304. Google ScholarDigital Library
- Toker, D., Conati, C., Carenini, G., & Haraty, M. Towards Adaptive Information Visualization: On the Influence of User Characteristics. Proc. Conf. on User Model., Adapt., and Personaliz., UMAP 2012, 274--285. Google ScholarDigital Library
- Townsend, J. T., & Ashby, F. G. (1983). Stochastic modelling of elementary psychological processes. London: Cambridge University Press.Google Scholar
- Turner, M. L., & Engle, R.W. Is working memory capacity task dependent? Journal of Memory and Language, 28(2), 127--154, 1989.Google ScholarCross Ref
- Van Zandt, T. How to fit a response time distribution. Psychonomic Bulletin & Review, 7(3), 424--465, 2002.Google ScholarCross Ref
- Velez, M. C., Silver, D., & Tremaine, M. Understanding visualization through spatial ability differences. Proc. of Visualization, 511--518, 2005.Google Scholar
- Wobbrock, J., Findlater, L., Gergle, D., & Higgins, J. The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures. Proc. Conf. on Human Factors in Comp. Sys., CHI 2011, 143--146. Google ScholarDigital Library
- Wolf, B. P. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann Publishers Inc., 2008. Google ScholarDigital Library
- Ziemkiewicz, C., Crouser, R. J., Yauilla, A. R., Su, S. L., Ribarsky, W., & Chang, R. How Locus of Control Influences Compatibility with Visualization Style. Proc. of IEEE VAST 2011, 81--90.Google ScholarCross Ref
Index Terms
- Highlighting interventions and user differences: informing adaptive information visualization support
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