Designing Introductory Materials for InfoVis

Designing introductory materials is extremely important when developing new information visualization techniques. Despite its significance, there has been little research on how to design effective introductory materials for information visualization. We use two concepts from educational psychology, learning type (or learning style) and teaching method, to design four unique types of online guides. The effects of the guides are measured by comprehension tests of a large group of crowdsourced participants. The tests covered four visualization types (graph, scatter plot, storyline, and tree map) and a complete range of visual analytics tasks. Our statistical analyses indicate that online guides which employ active learning and the top-down teaching method are the most effective. Our study provides quantitative insight into the use of exercise questions in online guides for information visualizations and will inspire further research on design considerations for other elements in introductory materials.


[PDF]: A Study On Designing Effective Introductory Materials for Information Visualization. Yuzuru Tanahashi, Nick Leaf, and Kwan-Liu Ma. In proceedings of Proceedings of Pacific Graphics . October, 2016

[Demo]: Example IVG with test: scatterplot + active + top-down

[Demo]: Full set of IVGs used for the user study


Yuzuru earned his Ph.D. from UC Davis in 2014.

Nick Leaf is a Ph.D. student focusing on Scientific Visualization. His current research interests include Image-based rendering, rendering Adaptive Mesh Refinement (AMR) data, and large-scale visualization. He earned his B.Sc. in Computer Science and Physics at the University of Wisconsin.

Kwan-Liu Ma is the head of VIDI Labs. He also chairs the Graduate Group in Computer Science (GGCS) of UC Davis. His research is in all areas of data visualization and he enjoys working with students to develop visualization solutions for new, challenging application problems.