Published today by Advance HE.
Advance HE supports universities in putting institutional strategy into practice for the benefit of students, staff and society.
Felicity Healey-Benson, FHEA, is a HR and Leadership Lecturer and Doctoral Candidate at University of Wales Trinity St. David. Her doctoral research interest is in responding to the impact of threats and opportunities of AI on teaching and learning to support the future talent pipeline for Workplace 4.0. In this blog, she explores the uses of visuals to understand data.
Who doesn’t love a quick ‘google’ for an image, to support a point from their perspective? An artistic prop, it can convey emotion or create dissonance, or just add that visual lift to content-stuffed communications!
As a fellow educator of the digital age, facing a growing population of tech-savvy and phone-centric students, I can identify with the modern classroom challenge of competing for attention whilst avoiding death by information! Imagery has for a long time been part of our arsenals, yet images alone won’t build student engagement, support understanding, or produce the requisite compelling story.
Recently I had access to the accomplished journalist & data visualisation (data vis) expert, Andy Pemberton, who shared powerful insights on effective design to support improved recipient understanding or simply, to more effectively persuade; outputs highly prized in both in education and research (Sherlock, 2016).
Data is ubiquitous now – like oil – but it does not mean much if it is in crude form. It needs to be refined to help people make decisions
– Pemberton (2017)
Visuals: the new academic power tool
Using pictures to understand data goes back centuries, but today’s new easy access, web-based techniques allows for non-specialists to create visually compelling analytics. ‘Data Vis’ and infographics, the product of a collection of data visualisations are set to support academics, both in the classroom and in research endeavours, to make better use of the increasing volume and complexity of information available to them (Exeter University, 2012).
However there is an important distinction to be made between the two similarly reported tools:
1. Infographics guide the audience to conclusions through a premeditated story (subjective).
2. Data visualisations assist audience withdrawing their own conclusions (objective) (Rogier, 2018)
Both are gaining credibility in scholarly communication, as scientists, researchers, and educators turn to computational visualizations to disclose patterns not so obvious presented in “non-visual formats” (Li et al., 2010). Perfect to facilitate brains to process comparisons, patterns, and differences more rapidly (Koch et al., 2006). Each with very different pedagogic opportunities and challenges.
I personally testify to the modern infliction of data overload, not just through multiple information channels accessed as a university lecturer and doctoral student, but through participation in life in general. It’s an affliction for us all. New tools that can help declutter and support focus, for a generation that appears desensitised to the noise of abundant data, is clearly welcome and should be championed within Higher Education.
McCandless (2010), data vis-expert of TedX fame, has an analogy of the utility of infographics offering relief in a landscape over-filled with a mind-numbing amount of information. And for me, ‘data vis’ goes one step further, offering a pathway for creating more digestible data. Furthermore, it’s a visually delightful method for seeing the unseen, enriching the process of scientific discovery, and fostering profound and unexpected insights (Gatto, 2015).
Simplified, yet sophisticated representations
Make no mistake though, these tools are strategically designed; more than charts created from quantitative data! They embed representations, using graphical and textual semiotic conventions of their creator’s understanding of a given issue (diSessa, 2002). Equally, the very process of their creation makes various demands on the author: effective design requires focus, and consolidation of the key themes, and further still, the skill to articulate conceptual understanding and present it visually. Interactive visualization takes it up a notch, users drill down into charts/graphs for a more detailed data experience. An effective medium for online sharing, an engaging means for academics to take their research messages to the masses, and even advocate change. This is clearly a priority area for staff development for most, if not all educational institutions. Not just covering off the basics of accessing and sharing expertise, but in how to critically appraise these new and developing inscriptional forms. More advanced support will be required from an academic standards and quality control perspective, ensuring higher education professional are consistent and fair in the way they assess and critique these evolving outputs, particularly when used in formal student submissions.
The future’s visually bright
It is claimed people with normal perceptual abilities are predominantly visual (Few, 2015). A simple tweak to lecture design, to harness these tools throughout our pedagogic practice may actually be the solution to make efficient use of increasing volumes of data, as well as support collective understanding, and facilitate wider use of our research. Possibly, next time, you reach for the internet, try a spot of infographic surfing, it’s an insightful exercise. Turn the tables on the students, get them to create their own infographics; to refine, to tease out insights and conclusions, and encourage their visual outputs to pique stakeholder interest. Even better, if you’re already progressed on this journey and have insight on how to embed these tools in your delivery or assess these outputs in your educational programmes, let’s start sharing.
The greatest value of a picture is when it forces us to notice what we never expected to see
– Tukey (1977)
The views expressed are the authors’ and do not necessarily reflect those of UWTSD.
Exeter University (2012) ‘Data Visualisation for Researchers and Scholars: Briefing Paper’ (online). Available at: https://as.exeter.ac.uk/media/universityofexeter/academicservices/educationenhancement/cascade/Data_visualisation_for_researchers_and_scholars.pdf [Accessed 1/12/2018].
diSessa. A. (2002) ‘Students’ criteria for representational adequacy’, Mathematics Education Library, 30, pp. 105–130.
Few, S. (2014) ‘Data Visualization for Human Perception’, The Encyclopaedia of Human-Computer Interaction, 2nd edition (online). Available at: https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception [Accessed 2/12/2018].
Gatto, M. A. C. (2015) ‘Making research useful: current challenges and good practices in data visualisation’, Reuters Institute for the Study of Journalism, University of Oxford.
Koch, K., McLean, J., Segev, R., Freed, M., Berry, M., Balasubramanian, V., & Sterling, P. (2006) ‘How Much the Eye Tells the Brain’, Current Biology, 16(14), pp. 1428–34.
Li, N., Brossard, D., Scheufele, D.A., Wilson, P.H., & Rose, K.M. (2018) ‘Communicating data: interactive infographics, scientific data and credibility’, Journal of Science Communication, 17(2), pp. 1-6.
McCandless, S. (2010) The beauty of data visualization . Available at: https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization [Accessed 27/11/2018]
Pemberton, A. (2017) Furthr’s director, Andy Pemberton, inspires LSST students and staff on data visualisation and a ‘broken’ social media (online). Available at: https://www.lsst.ac/news/furthrs-director-andy-pembertoninspires-lsst-students-and-staff-on-data-visualisation-and-a-broken-social-media/ [Accessed 28/11/2018].
Rogier, M. (2018) ‘Data Visualisations vs. Infographics’ [blog]. Available at: https://www.copypress.com/blog/data-visualizations-vs-infographics/ [Accessed 29/11/2018].
Sherlock, T. (2016) ‘The power of persuasion in the classroom’ (online). Available at: https://vancouversun.com/news/local-news/the-power-of-persuasion-in-the-classroom [Accessed 28/11/2018].
Tukey, J.W. (1977) ‘Exploratory Data Analysis’, Addison-Wesley: Reading.