What we're about

Data Visualization New York is one of the largest offline communities of data visualization professionals in the world. We gather designers, statisticians, analysts, programmers, mathematicians, data architects, start-up execs, content specialists, and all kinds of other amazing people from across the industry. We explore the full range of possibilities for how to convert raw data into visible, shareable insights.

Upcoming events (1)

Shaaron Ainsworth: Why Should We Learn to Draw?

Online event


In recent years, there has been increasing interest in asking learners to draw visual representations for themselves. When learners pick up a pencil and paper or move a stylus on a screen they can enhance their understanding. However, to date, most studies have focussed on a narrow range of practices based upon a predominantly information-processing approach to human cognition. In this talk, I argue that we need to develop a synthetic theoretical framework that understands learning at multiple timescales (from the millisecond to millennium) and levels (from the neuron to the society). Taking this approach leads us to recognize that drawing diagrams is not an optional "nice-to-have" but is fundamental to the way people learn. New knowledge emerges when drawing, as expressing what we currently know in external forms recruits cultural, cognitive, and sensory-motor resources that develop our own and others' understanding. Unsurprisingly, therefore we can draw to learn for many purposes: we draw to prepare, to observe, to remember, to understand and to communicate. In this talk, I illustrate these purposes using many drawings from diverse domains, address what successful drawing looks like in each case and what support learners might need. I will also consider several open questions, such as whether everyone can draw to learn and if there are certain situations where we should avoid drawing diagrams.

Shaaron Ainsworth is a Professor of Learning Sciences at the University of Nottingham with degrees in Psychology, AI and Cognitive Science. Her research interests focus on representational learning. She is particularly interested in visual and multimodal forms of learning and learning with representational technologies such as haptics, VR, collaboration tools and Serious Games. She has published around 100 papers and book chapters on these topics, supervised 20 Ph.D. and 100 MA students and collaborated with institutions around the globe.

Past events (66)

Jean-luc Doumont: Making sense of scales in graphs

Online event

Photos (95)

Find us also at