Educational Data Comics: What can Comics do for Education in Visualization?
Bibliographic record
Abstract
This paper discusses the potential of comics for explaining concepts with and around data visualization. With the increasing spread of visualizations and the democratization of access to visualization tools, we see a growing need for easily approachable resources for learning visualization techniques, applications, design processes, etc. Comics are a promising medium for such explanation as they concisely combine graphical and textual content in a sequential manner and they provide fast visual access to specific parts of the explanations. Based on a first literature review and our extensive experience with the subject, we survey works at the respective intersections of comics, visualization, and education: data comics, educational comics, and visualization education. We report on the potential of comics to create and share educational material, to engage wide and potentially diverse audiences, and to support educational activities. For each potential we list, we describe open questions for future research. Our discussion aims to inform both the application of comics by educators and their extension and study by researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".