VIDEO [40:24] of this talk can be seen at https://youtu.be/XBr8tQt7HoE BIO: Dr. Fanny Chevalier is an Assistant Professor at the Department of Computer Science, and Statistical Sciences at the University of Toronto, where she conducts research in data visualization and human-computer interaction. In particular, she has been interested in addressing the challenges involved in the design, implementation, and evaluation of novel interactive tools supporting visual analytics and creative activities, with primary focus on interactive tools for the visual exploration of rich and complex data, visualization education, the design and perception of animated transitions, and sketch-based interfaces. Prior to joining UofT, Dr. Chevalier was a Research Scientist at Inria, France until 2017, and in earlier years, a postdoctoral fellow at the University of Toronto, Ontario College of Art and Design University (OCADU), and Inria-Microsoft joint center in Paris. She obtained her PhD in Computer Science from the Université de Bordeaux in 2007. She is the recipient of an Inria grant for scientific excellence and her research papers have received awards at the premier venues in Human-Computer Interaction (ACM CHI, ACM UIST). Her work on sketch-based animation system from CHI 2014, released by Autodesk as SketchBook Motion has been awarded as the Apple iPad App of the Year for 2016. She has also consistently served in the organising and program committees for ACM CHI, ACM UIST and IEEE InfoVis conferences for the past years. ABSTRACT: Seeing Beyond the Chart: How to cultivate critical thinking through visualization. Data, “the oil of the digital era”, has come to be the most valuable resource of our modern society. As a scientist, I find it exciting to see news outlets using more and more data graphics to communicate facts, stakeholders increasingly rely on data analytics to gain insight into our world and make informed decisions, and governments increasingly promote and engage in open data. In the meantime, it is also daunting to witness how destructive fake news, rumours and falsehoods can be to a general population poorly prepared to engage in evidence-informed reasoning. In this talk, I will discuss one of the most important societal challenges of our times: visualization literacy, defined as the ability to understand, find, collect, interpret, and support arguments using visual representations of data. Through a sample of my recent research projects focusing on visualization creation, visual communication and visualization education, I will share my reflections on how we can cultivate an informed citizenry capable of critical thinking, reasoning, and knowledge-based decision making.

1. HOW TO CULTIVATE
CRITICAL THINKING
THROUGH VISUALIZATION
Seeing beyond the graph:
Fanny Chevalier
Assistant professor
University of Toronto

3. Winnpeg Sun, 2013The West Morland Gazette, 2015unknown university board

7. Map source: www.npr.org

8. Image source: REUTERS/Carlos Barria

10. Source: John Muyskens,Washington post, October 2016

11. Source: John Muyskens,Washington post, October 2016

12. How well do we understand data that we see everyday?
Relate: Using concrete scales to communicate complex measures
How well do we understand data this represented visually?
Educate:Visualization literacy at elementary school
How well can we communicate data visually?
Engage:Visualization literacy at elementary school
1.
2.
3.

13. How well do we understand
the data that we see everyday?
1.

14. 350 ml 475 ml 500 ml
39g of sugar 52g of sugar 65g of sugar
Recommended daily intake: 25g
(World Health Organization)

15. PRESENTATIONS
350 ml 475 ml 500 ml
39g of sugar 52g of sugar 65g of sugar
Recommended daily intake: 25g
(World Health Organization)

16. Image source: REUTERS/Shannon Stapleton

17. Video source: http://www.debtclock.ca/

18. USD$20.7 trillion
USD$ 20,760,852,675,267
USD$515 billion
CAD$ 650,881,545,273
USD$ 512,237,267,314
Retrieved on Feb.26, 10:45AM EST from: http://www.usdebtclock.org/ and http://www.debtclock.ca/

20. A concrete scale is a visualization
that conveys a complex measure by re-expressing it
using familiar objects from the real world
20 trillions of dollars59g of sugar
Using Concrete Scales:A Framework forVisual Depiction of Complex Measures
Chevalier, Vuillemot & Gali. IEEETVCG / Infovis 2013

21. COGNITIVE MECHANISMS
Anchor and
adjustment
Unitization
Analogy
0 measure
0 measure
0 measure
Using Concrete Scales:A Framework forVisual Depiction of Complex Measures
Chevalier, Vuillemot & Gali. IEEETVCG / Infovis 2013

22. How well do we understand
the data that we see everyday?
1.
Some measures may be difficult to grasp.
Relate data to more familiar concepts can help.

23. How well do we understand
data that is represented visually?
2.

25. Misleading visual representations

27. Perceptual biases

28. Image source:
NPR visual team.

29. VISUALIZATION LITERACY
ability to extract information from visualizations
and think critically about it
ability to communicated data in a visual form
as truthfully as possible

30. Where and when
do we learn these skills?

32. Qualitative coding of
textbook visual material
C’est la vis:Visualization Literacy at Elementary School
Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.

33. We encoded 2600 visual representations in
5000 textbook pages
C’est la vis:Visualization Literacy at Elementary School
Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.

34. Degree of Abstraction
Scott McCloud,
Understanding Comics
Degree of Abstraction
Scott McCloud,
Understanding Comics
C’est la vis:Visualization Literacy at Elementary School
Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.

35. C’est la vis:Visualization Literacy at Elementary School
Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.
Same data, different abstraction levels

36. C’est la vis:Visualization Literacy at Elementary School
Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.

37. Interested and focused
for 30 minutes
Disrupted class dynamics
Interacted a lot
Completed all activities
Peer learning
Verbalization
Retention

38. How well do we understand
data that is represented visually?
2.
Decoding and understanding visualizations is a
complex activity that requires knowledge and skill.
Visualization education can help.

39. How well can we communicate
data visually?
3.

40. VISUALIZATION LITERACY
ability to extract information from visualizations
and think critically about it
ability to communicated data in a visual form
as truthfully as possible

41. http://coloringclimate.com/

42. Color where carbon emissions from fossil fuels are the
greatest, which looks similar to a population density
map since this type of greenhouse gas is caused by
human activity.
http://coloringclimate.com/
See if you can color 20 football fields in under a
minute, which is about how fast we have been losing
global forests for the past 25 years

43. http://www.dear-data.com/

44. http://www.dear-data.com/

45. DataInk: Creative and Direct Data-Oriented Drawing
Xia, Riche, Chevalier, De Araujo,Wigdor.ACM CHI 2018 [Honourable mention]

46. DataInk: Creative and Direct Data-Oriented Drawing
Xia, Riche, Chevalier, De Araujo,Wigdor.ACM CHI 2018. [Honourable mention]

47. How well can we communicate
data visually?
3.
There are many challenges.
By reconciling creative drawing and data binding
we can engage people with data.

48. –Alberto Cairo
“[data is] always noisy, dirty, and uncertain.”

49. –Fanny Chevalier
“… and so is the way
we portray and perceive it.”

50. Relate
Educate
Engage
1.
2.
3. http://fannychevalier.net