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Record W4392681555 · doi:10.22318/icls2023.831011

Data Comics: Using Narratives to Engage Students in Data Reasoning

2023· article· en· W4392681555 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsYork University
FundersNational Science Foundation
KeywordsComicsNarrativeCurriculumQualitative propertyComputer scienceThematic analysisStorytellingMathematics educationPsychologyPedagogySociologyQualitative researchArtLiteratureSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.732
GPT teacher head0.584
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it