Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Creating Data Art: Authentic Learning and Visualisation Exhibition

Jonathan C. Roberts

TL;DR

This paper investigates how to teach data visualization to computing students through an authentic learning task that culminates in a public data-art exhibition. It presents a seven-task framework integrated into the ICE3121 Creative Visualisation module, leveraging Processing and the Five Design-Sheets to guide topic selection, design, production, and reflection. Across two cohorts and a two-year cycle, it reports a two-week public exhibition and 33 unique artworks, with evidence of increased engagement and practical skills transfer to real-world contexts. The study demonstrates a scalable approach to bridging theory and practice by combining data analysis, artistic design, and professional presentation in a public-facing format.

Abstract

We present an authentic learning task designed for computing students, centred on the creation of data-art visualisations from chosen datasets for a public exhibition. This exhibition was showcased in the cinema foyer for two weeks in June, providing a real-world platform for students to display their work. Over the course of two years, we implemented this active learning task with two different cohorts of students. In this paper, we share our experiences and insights from these activities, highlighting the impact on student engagement and learning outcomes. We also provide a detailed description of the seven individual tasks that learners must perform: topic and data selection and analysis, research and art inspiration, design conceptualisation, proposed solution, visualisation creation, exhibition curation, and reflection. By integrating these tasks, students not only develop technical skills but also gain practical experience in presenting their work to a public audience, bridging the gap between academic learning and professional practice.

Creating Data Art: Authentic Learning and Visualisation Exhibition

TL;DR

This paper investigates how to teach data visualization to computing students through an authentic learning task that culminates in a public data-art exhibition. It presents a seven-task framework integrated into the ICE3121 Creative Visualisation module, leveraging Processing and the Five Design-Sheets to guide topic selection, design, production, and reflection. Across two cohorts and a two-year cycle, it reports a two-week public exhibition and 33 unique artworks, with evidence of increased engagement and practical skills transfer to real-world contexts. The study demonstrates a scalable approach to bridging theory and practice by combining data analysis, artistic design, and professional presentation in a public-facing format.

Abstract

We present an authentic learning task designed for computing students, centred on the creation of data-art visualisations from chosen datasets for a public exhibition. This exhibition was showcased in the cinema foyer for two weeks in June, providing a real-world platform for students to display their work. Over the course of two years, we implemented this active learning task with two different cohorts of students. In this paper, we share our experiences and insights from these activities, highlighting the impact on student engagement and learning outcomes. We also provide a detailed description of the seven individual tasks that learners must perform: topic and data selection and analysis, research and art inspiration, design conceptualisation, proposed solution, visualisation creation, exhibition curation, and reflection. By integrating these tasks, students not only develop technical skills but also gain practical experience in presenting their work to a public audience, bridging the gap between academic learning and professional practice.
Paper Structure (8 sections, 5 figures)

This paper contains 8 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Background & Related work
  3. Authentic learning, creative thought and visualisation
  4. Data art
  5. Teaching materials and lectures
  6. The seven tasks
  7. Scenario and exhibition
  8. Discussion and Conclusion

Figures (5)

  • Figure 1: For the exhibition's authentic task, we divided the students' work into two phases: first, to understand the topic and create suitable designs; second, to build the solution, deliver it for the curated exhibition, and reflect on their accomplishments.
  • Figure 2: Data art examples. See Appendix 1 for full credits. 1) Aaron Koblin, 2) Nadieh Bremer (2020). 3) Federica Fragapane (2009), 4) Kirell Benzi (2016), 5) Kirel Benzi (2020). 6) Ken Flerlage. 7) Cristian Vasile (2014) 8) Martin Krzywinski (2016) 9) Jonathan Harris and Sepandar Kamvar.
  • Figure 3: World happiness data, shown in Chernoff face style glyphs, using processing.org. The overall happiness value corresponds to both the depth of the smile and the facial colour. Social support perception is represented by smile width, generosity is indicated by brow length, GDP is reflected in the overall face size, and life expectancy is shown through ear length. Design, data processing and artwork by author.
  • Figure 4: The soundscape data-art work, depicts sounds from the environment. This figure shows a lift being called, travelling in the lift, two wobbles as the lift nears its floor, and the doors opening. Design, data processing and artwork by author.
  • Figure 5: One part of the exhibition, showing some of the exhibits in the foyer of the public innovation, cinema and theatre building in Bangor, UK.