How do you make sense of a graph that you have never seen before?
As powerful as graphics may be in their communicative efficiency, they needn’t be immediately easy to understand. In fact, there are often real tradeoffs between a representation’s discoverability and efficiency. Peebles & Cheng (2003) demonstrated that even for informationally equivalent graphs, the computational efficiency (operationalized in terms of the number of steps to retrieve information) of a less conventional representation may outweigh its ease of use to the untrained reader. It is this fact that underlies much modern research in Information Visualization: developing sophisticated information interfaces for highly skilled workers performing complex and specialized tasks. Sometimes this work results in novel, unconventional representations that are computationally suited to a particular set of data and analytical task, but that present a graph-reading challenge to the lay reader. Most work in remediating errors in graph comprehension has focused on “second order” readings: characterizing the trend or relationships between data points in a graph. In the case of time interval graphs this would involve generalizations like, “most events occur late in the day”, or “longer events start earlier in the day”. We tend to accept a priori that well-designed graphs readily afford first-order readings: operations for extracting data from the graph. In fact, it is the seeming effortlessness of these readings that make graphs so desirable as external representations. In this project, we use a simple but unconventional graph - The Triangular Model (TM) of Interval Relations (Qiang et. al. 2014) to explore how individuals make use of prior knowledge when reading unconventional graphs.

In Study 1 we observe learners solving problems with the TM graph, and challenge them to design instructional aids.
In Study 2 we evalute efficacy of four scaffolding techniques with both conventional and TM graphs.


WIP Interactive Mouseflow Visualization

Researchers
Amy Rae Fox, MA MEd, PhD student of Cognitive Science, UC San Diego
James Hollan, PhD, Professor of Cognitive Science, UC San Diego

Caren Walker, PhD, Assistant Professor of Psychology, UC San Diego
Research Assistants: Evan Barosay, Hazel Leung, Alexis Flores, Kai-yu Chang

Publications
Fox, A. R., Hollan, J. (2018). Read it This Way: Scaffolding Comprehension for Unconventional Statistical Graphs
In Diagrammatic Reasoning & Inference. Lecture Notes in Computer Science: Springer International Publishing. Presented at the 2018 Conference on Theory and Application of Diagrams

Fox, A. R., Walker, C., Hollan, J. (August, 2018). Graphical Insight: How to Read an Unconventional Graph.
European Association of Research on Learning and Instruction - SIG 2. Freiburg, Germany. Presented at the 2018 Conference for the EARLI SIG2 group on Comprehension of Text and Graphics

Fox, A.R., Hollan, J., Walker, C. (July, 2019). When Graph Comprehension Is An Insight Problem.
Proceedings of the Annual Meeting of The Cognitive Science Society
Presented at the 2019 Annual Conference of the Cognitive Science Society, Montreal Canada.

References
1. Peebles, D., & Cheng, P. C.-H. (2003). Modeling the effect of task and graphical representation on response latency in a graph reading task. Human Factors, 45(1), 28–46. https://doi.org/10.1518/hfes.45.1.28.27225
2. Qiang, Y., Valcke, M., De Maeyer, P., & Van de Weghe, N. (2014). Representing time intervals in a two-dimensional space: An empirical study. Journal of Visual Languages and Computing, 25(4), 466–480. https://doi.org/10.1016/j.jvlc.2014.01.001


Copyright © 2018 Amy Rae Fox. All rights reserved.