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

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.

A Didactic Framework for Analyzing Learning Activities to Design InfoVis Courses.

Data visualization is a powerful tool to cope with the demands of our current information age. In order to understand and be able to develop visualizations for specific use cases, data visualization activities (vis activities) have been proposed in recent years. These highly effective tools focus on practical relevance, reflection, and discussion in order to teach data visualization knowledge in a variety of contexts. However, the conscious selection of one or more vis activities for learners in comprehensive courses remains difficult. We aim to support this process by proposing a didactic vis framework. Based on Bloom's revised learning taxonomy, we decompose vis activities into distinct learning activities with their specific learning goals. By assigning the learning goals to the cognitive process and knowledge dimensions, a didactic course structure can be planned and evaluated. To demonstrate this didactic vis framework, we conducted several workshops based on an existing interface construction kit.

Glyphboard: Visual Exploration of High-Dimensional Data Combining Glyphs with Dimensionality Reduction.

Rigorous data science is interdisciplinary at its core. In order to make sense of high-dimensional data, data scientists need to enter into a dialogue with domain experts. We present Glyphboard, a visualization tool that aims to support this dialogue. Glyphboard is a zoomable user interface that combines well-known methods such as dimensionality reduction and glyph-based visualizations in a novel, seamless, and integrated tool. While the dimensionality reduction affords a quick overview over the data, glyph-based visualizations are able to show the most relevant dimensions in the data set at one glance. We contribute an open-source prototype of Glyphboard, a general exchange format for high-dimensional data, and a case study with nine data scientists and domain experts from four exemplary domains in order to evaluate how the different visualization and interaction features of Glyphboard are used.