- Reflections and Considerations on Running Creative Visualization Learning Activities - Roberts, Jonathan C.; Bach, Benjamin; Boucher, Magdalena; Diehl, Alexandra; Hinrichs, Uta; Huron, Samuel; Kirk, Andy; Chevalier, Fanny; Knudsen, Søren; Meirelles, Isabel; Noonan, Rebecca; Pelchmann, Laura; Rajabiyazdi, Fateme; Stoiber, Christina - Cornell University : arXiv.org, 2022. - 8 pp..
- EduVis : Workshop on Visualization Education, Literacy, and Activities - Keck, Mandy; Huron, Samuel; Panagiotidou, Georgia; Rajabiyazdi, Fateme; Perin, Charles; Bach, Benjamin; Roberts, Jonathan C.; Rajabiyazdi, Fateme - Cornell University : arXiv.org, 2023. - 5 pp..
- Challenges and Opportunities in Data Visualization Education : A Call to Action : author’s version of the article - Carpendale, Sheelagh; Roberts, Jonathan C.; Hinrichs, Uta; Aerts, Jan; Knudsen, Soren; Boucher, Magdalena; Aigner, Wolfgang ; Perin, Charles; Huron, Samuel; Stoiber, Christina; Laramee, Robert S.; Dykes, Jason; Meirelles, Isabel; Rajabiyazdi, Fateme; Keck, Mandy; Bach, Benjamin; Losev, Tatiana; AlKadi, Mashael; Morais, Luiz; Kosminsky, Doris; Manataki, Areti - Los Alamitos : IEEE, 2023. - 14 pp. - (IEEE transactions on visualization and computer graphics ; 2023).
- Me-ifestos for Visualization Empowerment in Teaching (and Learning?) - Aerts, Jan; Bishop, Fearn; Hayes, Sarah; Kinkeldey, Christoph; Walny, Jagoda; Willett, Wesley; Landesberger, Tatiana von; Stoiber, Christina; Rajabiyazdi, Fateme; Pelchmann, Laura; Panagiotidou, Georgia; Noonan, Rebecca; Nagel, Till; Morais, Luiz; Meirelles, Isabel; Manches, Andrew; Losev, Tatiana; Kosminsky, Doris; Manataki, Areti; Knudsen, Soren; Kirk, Andy; Huron, Samuel; Hinrichs, Uta; Dykes, Jason; Diehl, Alexandra; Cheng, Peter C. H.; Boucher, Magdalena; Bach, Benjamin; Aigner, Wolfgang - Copenhagen : Univ. Copenhagen, 2022. - 12 pp..
This seminar set out to discuss timely issues and approaches to teaching and education in data visualization. The topic is of growing importance in a world where more and more content is being shared through online news and social media. Our mission as researchers, practitioners and educators in data visualization is to assure quality education for everyone engaging with visualization; this ranges from visualization designers, data scientists, school teachers, journalists, working professionals, students, as well as general public audiences. Teaching visualization is tricky for a range of reasons:
- Data visalization is a skill that is only slowly starting to make its way into school curricula (at least in some countries);
- While the range of visualization tools available makes it easy for almost anyone to create visualizations regardless of their technical background, it can be overwhelming to know where to start and to navigate this ever-growing and changing tool landscape;
- Data visualization is a highly interdisciplinary field, influenced and moved forward by psychology, cognitive science, design, computer science, data science, art, and many more disciplines. As a result, learning objectives and teaching practices greatly vary;
- There is currently no defined agreement on the learning goals and criteria for visualization literacy. For example, what defines a beginner, intermediate, professional in data visualization? What aspects of data visualization should be taught at different levels? And: how can we assess visualization skills?;
- From a learner perspective, the motivation to pick up visualization as a skill is broad: some people "just" want to use a specific tool to get things done quickly, others pursue a design approach (no coding language required), others want to build systems for visualization (Computer Sciences), others go on and become educators or researchers;
- Visualization is important in many domains and knowledge and specific solutions might be specific to these domains, rather than valid universally (e.g., color choices, symbolic conventions, level of interactivity);
- There are a lot of tacit knowledge and skills involved in visualization which can be difficult to pin down and transform into learning activities.
In order to discuss these challenges and how to navigate them, we invited participants from academia and industry, including senior and junior thinkers.
Participants & Seminar Format
Given the highly interdisciplinary field of data visualization and visualization literacy, participants covered a range of expertises including the fields of design, computer science, human-computer interaction, education, graphics, and cognitive psychology. The 5-day seminar was run in a hybrid format with 28 participants joining us at Schloss Dagstuhl in-person, 7 participants joining us online synchronously from Europe, and 6 participants joining us asynchronously from North America. Two organizers were on-site at Schloss Dagstuhl, while two joined the seminar remotely (one synchronously and one asynchronously). One of the online organizers led the asynchronous North America group from Canada. All seminar participants (synchronous and asynchronous) met for a daily debriefing session at 5pm local (Dagstuhl) time to share their progress and discussions. The synchronous remote participants (Europe) joined different local discussion groups through online calls, which did work out surprisingly well - special thanks to the Dagstuhl technical team for the amazing help with the hybrid setup.
Seminar Structure & Activities
The seminar followed an open-ended approach with respect to the possible outcomes, to allow discussion topics to emerge and develop, based on participants' expertise and interests. Discussions were sparked by brief talks and visualization activities led by selected participants.
The seminar talks included presentations on visualization teaching and learning with children, a syntactic analytical framework for visualization, engaging new students with visualization, using forums to engage students with visualization content, how to approach and streamline large-scale assessment of university students' visualization projects, as well as an overview over a book project from a past Dagstuhl Seminar (find the complete list and abstracts of talks in Section 5).
From a practical end, the visualization activities invited seminar participants to actively engage in and experience a number of visualization teaching methods and techniques (see section 6 for more details). One activity invited participants to sketch their relation to the seminar topic in order to introduce participants to each other and to start immersing them into the seminar topic. Another activity asked participants to analyze a given visualization systematically. In one activity, we classified existing visualization activities that were submitted by participants prior to the seminar. Another activity took a speculative approach to visualization, inspiring critical visualization scenarios and designs through a card game.
There was ample time to discuss topics of interests through breakout groups which focused on topics related to
- Teaching methods and taxonomies for educational activities;
- Teaching creativity and criticality for visualization;
- Data physicalization and how corresponding methods can be used for education and engagement;
- Practical approaches to teaching visualization and the politics involved in teaching visualization;
- Approaches to visualization teaching and creation inspired by improvisation in the arts, and eventually;
- Grand challenges in visualization education.
From an organizer perspective, the seminar was a great success. All participants - both on-site and online - were extremely engaged, and we obtained very positive feedback. Participants appreciated the creative and open-ended nature of this seminar that invited for sharing and reflection of practices from different disciplines and perspectives. The seminar produced a long list of outcomes ranging from paper outlines and book projects, to collecting teaching manifestos and taxonomies, to grant projects and platforms for sharing teaching tools and resources. The plan emerged to establish a reoccurring international symposium around visualization education as part of the IEEE VIS conference, the largest annual conference on visualization with over 1000 participants. The individual working groups will move their individual goals forwards after the seminar. As organizers, we will coordinate between groups and support each of the projects as best as we can, e.g., through regular check-ins with the workgroup leaders as well as townhouse meetings with all Dagstuhl participants, e.g., once a semester. We all believe strongly that this Dagstuhl Seminar - the first formal event on visualization education besides smaller conference workshops -- has created a strong momentum for visualization empowerment and education, and we are looking forward to sharing our outcomes on a dedicated website soon.
The concept of visualization literacy encompasses the ability to read, write, and create visualizations of data using digital or physical representations and is becoming an important asset for a data-literate, informed, and critical society. While many useful textbooks, blogs, and courses exist about data visualization—created by both academics and practitioners—little is known about 1) how learning processes in the context of visualization unfold and 2) what the best practices are to engage and to teach the theory and practice of data visualization to diverse audiences, ranging from children to adults, from novices to the advanced, from students to professionals, and including different domain backgrounds.
The aim of this Dagstuhl Seminar is to collect, discuss, and systematize knowledge around the education and teaching of data visualization to empower people making effective and unbiased use of this powerful medium. To that end, we aim to:
- Provide a cohesive overview of the state-of-the-art in visualization literacy (materials, skills, evaluation, etc.) and compile a comprehensive handbook for academics, teachers, and practitioners;
- Collect and systematize learning activities to inform teaching visualization across a wide range of education scenarios in the form of a teaching activities cook-book.
- Discuss open challenges and outline future research agendas to improve visualization literacy and education. We aim to facilitate interdisciplinary research collaborations among attendees; researchers, practitioners, and educators from a wide range of backgrounds including data visualization, education, and data science.
The research questions we would like to discuss relate to four themes:
- Content of teaching and education, including learning goals, concepts, skills, practical and theoretical knowledge. Which resources, materials, and best practices exist today to teach visualization?
- Context and factors that define and inform the content and process of teaching and learning: audiences, their background and learning objectives, scenarios such as university, school, self-taught, and online learning, workshops in industry, at science fairs or other venues.
- The process of teaching, learning, and practicing visualization skills and questions about how we teach, learn, and support learners in individual self-paced learning, peer-learning as well guided by active teaching. The process also includes dedicated teaching materials in the form of, e.g., textbooks, reading lists, activities, cheat sheets, interactive experiences, data sets, and visualization tools.
- Assessment of the visualization education materials and processes as well as the assessment of students, learning objectives, and progress.
The outcomes of the seminar and our discussions aim to have an impact on visualization research and education, education theory and practice, as well as policy making.
- Jan Aerts (Amador Bioscience - Hasselt, Hasselt University & KU Leuven, BE) [dblp]
- Fearn Bishop (BBC - Salford, GB)
- Peter C.-H. Cheng (University of Sussex - Brighton, GB) [dblp]
- Alexandra Diehl (Universität Zürich, CH)
- Jason Dykes (City - University of London, GB) [dblp]
- Sarah Hayes (Munster Technological University - Cork, IE) [dblp]
- Uta Hinrichs (University of Edinburgh, GB) [dblp]
- Trevor Hogan (Munster Technological University - Cork, IE) [dblp]
- Christoph Huber (Hochschule Mannheim, DE)
- Samuel Huron (Institut Polytechnique de Paris, FR) [dblp]
- Mandy Keck (Univ. of Applied Sciences - Hagenberg, AT)
- Christoph Kinkeldey (HAW - Hamburg, DE)
- Søren Knudsen (IT University of Copenhagen, DK) [dblp]
- Doris Kosminsky (University of Rio de Janeiro, BR)
- Tatiana Losev (Simon Fraser University - Burnaby, CA)
- Areti Manataki (University of St Andrews, GB)
- Isabel Meirelles (The Ontario College of Art and Design University, CA) [dblp]
- Luiz Morais (INRIA - Bordeaux, FR)
- Till Nagel (Hochschule Mannheim, DE) [dblp]
- Rebecca Noonan (Munster Technological University - Cork, IE)
- Georgia Panagiotidou (University College London, GB) [dblp]
- Laura Pelchmann (Universität Köln, DE)
- Fateme Rajabiyazdi (Carleton University - Ottawa, CA)
- Jonathan C. Roberts (Bangor University, GB) [dblp]
- Christina Stoiber (FH - St. Pölten , AT)
- Jagoda Walny (Canada Energy Regulator - Calgary, CA) [dblp]
- Wesley J. Willett (University of Calgary, CA) [dblp]
- Wolfgang Aigner (FH - St. Pölten , AT) [dblp]
- Mashael Alkadi (University of Edinburgh, GB)
- Benjamin Bach (University of Edinburgh, GB) [dblp]
- Michael Baker (Telecom Paris & CNRS i3 Lab)
- Magdalena Boucher (FH - St. Pölten , AT)
- Emeline Brulé (University of Sussex - Brighton, GB)
- Sheelagh Carpendale (Simon Fraser University - Burnaby, CA) [dblp]
- Mine Çetinkaya-Rundel (Duke University - Durham, US)
- Fanny Chevalier (University of Toronto, CA) [dblp]
- Marti Hearst (University of California - Berkeley, US) [dblp]
- Nathalie Henry Riche (Microsoft Research - Redmond, US) [dblp]
- Andy Kirk (Visualising Data - Leeds, GB)
- Robert S. Laramee (University of Nottingham, GB) [dblp]
- Andrew Manches (University of Edinburgh, GB)
- Dietmar Offenhuber (Northeastern University - Boston, US) [dblp]
- Charles Perin (University of Victoria, CA) [dblp]
- Alison Powell (London School of Economics, GB) [dblp]
- Human-Computer Interaction
- Other Computer Science
- Information Visualization
- Visualization Literacy
- Data Literacy