EduVis: 4th IEEE VIS Workshop on Visualization Education, Literacy, and Activitiesbookmark_border

We share “1 Dataset, 5 Visualizations” (1d5v), a peer-oriented assignment model developed and deployed across two increasingly large offerings of an undergraduate visualization course (57 and 94 students). Each week, students individually author at least five distinct visualizations of a shared dataset using an assigned tool, then engage in structured peer ranking, written critique, and in-person small-group discussion that culminates in an instructor-led debrief. This format seeks to address converging pressures faced by data visualization courses—which are often seeing growing enrollments just as generative AI tools have unsettled traditional take-home assignments and assessment practices. Our 1d5v model couples high-volume design authoring with a deliberate progression of tools—from hand sketching and physical construction through GUI charting tools to code-based libraries—and rotating, randomized teams. We describe the model’s design rationale and weekly structure, and reflect on our experience from two course iterations. Specifically, we highlight how 1d5v assignments can enable greater practical engagement with design and critique and how combining manual and GUI-based authoring with in-person critique can support more transparent and generative assessment. We close by identifying concrete opportunities for purpose-built tooling, particularly to support ranking and presentation of large numbers of student visualizations, that current learning management systems don't address.

Visualization pedagogy often asks students to make choices that extend beyond representation alone. Yet as visualization educators, we have repeatedly encountered moments in our own teaching where the relationship between a visualization's outward form and what shapes its production remains implicit. In such moments, students may learn to reproduce conventions without fully engaging with the assumptions and values that gave rise to them. We describe this relationship as one between surface (what a visualization looks like and appears to claim) and substance (the processes, values, and commitments that produced it). Drawing on collaborative reflective inquiry, we present eight situated, first-person reflections on moments from our own teaching where surface and substance came into question. Reading across these cases, we also present four tensions concerning intentionality, interpretation, pedagogical structure, and misalignment. Rather than resolving these tensions, we seek to trouble our teaching practices and examine the relative ease of surface over substance woven into how we teach, from the tools and examples we bring into the classroom to how we structure and evaluate student work. We conclude with reflective probes to help educators examine these tensions within their own teaching, and open a wider discussion within the community.

Although data visualizations are increasingly present in school curricula, students typically encounter ready-made charts rather than creating their own. Even when opportunities for construction exist, they often rely on template-based tools that require learners to choose among predefined chart types instead of making explicit representational decisions. In this paper, we report on the design and implementation of a learning experience in which middle school students used the programming tool PlayData to construct visualizations from an earthquake dataset as a way to investigate plate tectonics and engage with data practices. Drawing on classroom video recordings and students' projects from a summer workshop, we examine the questions students posed, the representations they created, and the discussions that emerged. We observed that visualization construction supported learners' epistemic agency and that rich discussions emerged from visualizations that had not been anticipated by the activity designers, including aspects such as data provenance and measurement practices. However, creating a visualization did not automatically lead students to interpret it, suggesting that these activities require deliberate support to move from representations to explanations. We discuss affordances and challenges of open-ended visualization activities and argue that visualization creation and ready-made visualizations may support complementary learning goals.

Visualization literacy assessment tests such as VLAT and Mini- VLAT measure people’s ability to perform isolated, low-level visual analytics tasks, such as estimating statistical quantities. However, it remains unclear whether these assessments also account for the overall understanding viewers intuitively gain about the data with- out explicit cuing or guidance, often referred to as high-level com- prehension. In this work, we examine whether Mini-VLAT scores predict high-level comprehension, characterized as alignment be- tween viewers’ uncued interpretations and designer intent. We con- ducted a study with 80 participants, in which each participant com- pleted the Mini-VLAT test and interpreted 16 charts selected from a set of 40 real-world visualizations. Our qualitative and quantita- tive analyses show no significant relationship between participants’ Mini-VLAT scores and high-level comprehension. Our findings suggest while visualization literacy tests like Mini-VLAT capture viewers’ ability to perform directed, low-level tasks, they do not as- sess high-level comprehension in natural, uncued settings. Future work should develop VLAT that accounts for high-level compre- hension beyond estimating prespecified statistical quantities.

Chart taxonomies are widely used in introductory data visualization education to support chart selection. Their pedagogical value, however, remains debated: while they may reduce cognitive load, they have also been criticized for encouraging rigid, template-driven reasoning. This study reframes that debate through instructional scaffolding and examines whether chart taxonomies can function as temporary supports that improve performance without creating strong scaffold dependence. 29 students in an introductory data visualization course completed a baseline assessment, taxonomy-supported practice, and a delayed post-test without taxonomy access. Chart-selection accuracy increased from 67.2% at baseline to 78.3% at post-test, with a significant phase effect, F(2, 56) = 7.10, p = .002. Decision time decreased, while confidence and perceived workload remained relatively stable. Analysis of written justifications from 22 students showed a modest, nonsignificant increase in reasoning quality. Exploratory analysis of complex and creativity-oriented post-test tasks showed that students frequently selected multiple charts, combined chart types across taxonomy categories, and produced largely defensible solutions with substantive reasoning. Although these tasks had no pre-test counterparts, they provide descriptive evidence of flexible taxonomy use in more complex contexts. Overall, the findings are consistent with chart taxonomies functioning as fading scaffolds rather than rigid templates.

Visualization frameworks have operationalized literacy as an individual competency, measured through chart-comprehension tasks. This focus raises a question: why has measurement become the frame for understanding literacy? Rather than asking whether literacy should be measured, we ask how measurement became the field's way of understanding it. We trace visualization literacy through the history of textual literacy and argue that the field imported three values rooted in government statistics: quantification, individualization, and binary classification. To mark the shift, we distinguish two waves: a first centered on assessment and individual proficiency, and a second that treats literacy as a situated practice, attentive to how people use visualizations in context. Examining the construct suggests that refining assessment instruments does not settle what literacy is, and points to directions for second-wave research: formative studies of literacy, culturally grounded instruments, and critical reading. This paper shows that visualization literacy inherits its measurement frame, offers a two-wave vocabulary that distinguishes assessment-based literacy from situated practice, and connects the field to a critical-literacy tradition with alternatives to measurement. These contributions allow us to treat measurement and meaning as two parts of a question, shifting attention from who counts as literate to what literacy is meant to do.

Visualization authors can use a number of tools to design and author data graphics, including those with alternative input modalities. For example, sketching is useful for exploring ideas, natural language for expressing intents that are not easily visually expressible, and direct code editing for precise refinement. Prior work exploring these modalities has attended to one modality at a time. However, a data visualization designer may benefit from the ability to fluidly move between modalities for different stages of the design process. We present Inkling, a prototype multimodal editor for data visualization in which users can combine sketching, natural language instructions, and direct code editing to design visualizations that are bound to their data. We conducted pilot user studies with six visualization novices to gain an initial insight into how they used the three modalities to complete assigned visualization tasks. This work is meant to inform further larger-scale empirical studies into the effectiveness of multimodal visualization editing. Our qualitative analysis of screen recordings and audio transcriptions revealed consistent patterns of usage of the three interaction modes.

Educational escape rooms have emerged as a promising game-based learning format to engage students with complex subject matter in a motivating setting. Data visualisation literacy is an increasingly important skill for students in a data-driven society, yet it remains underrepresented in formal education. In this paper, we present Snailpocalypse, a physical escape room designed to foster data visualisation literacy among secondary education students aged 13–17. Following an iterative, user-centred design approach, we developed three puzzle stations, each embedding a different chart type within a narrative about a catastrophic snail virus outbreak, and a final puzzle: a combination-lock box containing small gifts. Each combination locks open with a pin code obtained by solving the three puzzles. We evaluated the escape room in three playtesting sessions with different participant groups: four visualisation experts, 19 secondary school students in Linz, Austria, and 12 visitors to a science outreach event at a university. The results indicate consistently high ratings for enjoyment, immersion, and teamwork across all three groups, while self-reported learning outcomes were moderate, pointing to the need for formal pre- and post-test evaluations in future work. All materials are open-source, enabling educators to deploy and adapt the game for classrooms of 20–30 students.