How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking
Issue No. 01 - Jan. (2016 vol. 22)
Sukwon Lee , School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Sung-Hee Kim , Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
Ya-Hsin Hung , School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Heidi Lam , , Google Inc., Mountain View, CA, USA
Youn-Ah Kang , Techno-Art Division, Information and Interaction Design, Incheon, South Korea
Ji Soo Yi , School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information Vlsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities:
encountering visualization, constructing a frame, exploring visualization, questioning the frame, and floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.
Data visualization, Visualization, Encoding, Interviews, Hidden Markov models, Image color analysis, Vehicles
S. Lee, S. Kim, Y. Hung, H. Lam, Y. Kang and J. S. Yi, "How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking," in IEEE Transactions on Visualization & Computer Graphics, vol. 22, no. 1, pp. 499-508, 2016.