TY - JOUR

T1 - Challenges and Opportunities in Data Visualization Education: A Call to Action

AU - Bach, Benjamin

AU - Keck, Mandy

AU - Rajabiyazdi, Fateme

AU - Losev, Tatiana

AU - Meirelles, Isabel

AU - Dykes, Jason

AU - Laramee, Robert S.

AU - AlKadi, Mashael

AU - Stoiber, Christina

AU - Huron, Samuel

AU - Perin, Charles

AU - Morais, Luiz

AU - Aigner, Wolfgang

AU - Kosminsky, Doris

AU - Boucher, Magdalena

AU - Knudsen, Søren

AU - Manataki, Areti

AU - Aerts, Jan

AU - Hinrichs, Uta

AU - Roberts, Jonathan C.

AU - Carpendale, Sheelagh

PY - 2023/8/9

Y1 - 2023/8/9

N2 - This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper—educators and researchers in data visualization—identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

AB - This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper—educators and researchers in data visualization—identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

KW - Data Visualisation

KW - Education

KW - pedagogic research

KW - Challenges

KW - Grand challenges

KW - Information Visualisation

KW - Scientific Visualisation

KW - Diversity

KW - Inclusion

KW - Responsible innovation

M3 - Article

JO - IEEE Transactions on visualization and computer graphics

JF - IEEE Transactions on visualization and computer graphics

SN - 1077-2626

T2 - IEEE VIS: Visualization & Visual Analytics

Y2 - 22 October 2023 through 27 October 2023

ER -