Data Comics: Using Narratives to Engage Students in Data Reasoning
Bibliographic record
Abstract
Comics are a familiar art form that has been underexplored as a tool for data-driven storytelling in K12 classrooms. Making data comics provide an opportunity for students to contextualize data within a visual style and narrative structure. This paper focuses on the second-year implementation of an interdisciplinary curriculum in seventh grade classrooms with one art and one math teacher. Students compared sample data from a national survey conducted by Pew Research Center, a nonpartisan US-based think tank, and data taken from their own survey on friendship perceptions and experiences. Students created comics based on those data using Pixton, a digital comic-making tool. Our study asks: How do students' use narratives to demonstrate different kinds of data reasoning? Thematic analysis of 47 data comics revealed the ways students constructed narratives, showcasing how the comic-making process cultivated students' reasoning around data and their informal inference-making skills.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".