Data Comics: Using Narratives to Engage Students in Data Reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it