My first reading comes from Michael Hohl, From Abstract to Actual: art and designer-like enquirers into data visualization.
Hohl describes how “multi-disciplinary collaboration may be key to find such a balance between complexity of data, clarity of conveyance and ambiguous-aesthetic qualities which stimulate individuals’ imaginations” (2011, p. 2).
Hohl further alludes to the work each person contributes to the end product aides not only in the learning of people outside the research piece, but also assists their own learning about fields which they previously may not have known “for example, physicists learn about music, while musicians get deeper insights into physics and astronomy” (2011, p. 2).
The second; The Big Picture for Big Data: Visualization article talks about how a healthcare researcher identified a clue using a data visualisation technique, which with statistical confirmation led to an important scientific breakthrough (Schneiderman, 2014).
This style of data visualisation Schneiderman explains, helps scientists/doctors etc. by exploring complex data which can then lead to more potent and meaningful insights (Schneiderman, 2014, p. 730).
Comparing the two, both Hohl and Schneiderman identify how the use of data visualisation can be key in helping to teach – but down two completely different paths.
Hohl, M. (2011). From abstract to actual: art and designer-like enquiries into data visualisation, Kybernetes, 40, 7-8, 1038-1044. Retrieved from https://www.academia.edu/1251934/From_abstract_to_actual_art_and_designer-like_enquiries_into_data_visualisation
Schneiderman, B. (2014). The Big Picture for Big Data: Visualization, SCIENCE, Vol. 343 no. 6172 p. 730. DOI: 10.1126/science.343.6172.730-a.