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Record W3177433891 · doi:10.1016/j.tate.2021.103416

Fostering creativity in science learning: The potential of open-ended student drawing

2021· article· en· W3177433891 on OpenAlexaff
Alandeom W. Oliveira, Adam O. Brown, Wo Su Zhang, Patrick LeBrun, Liam Eaton, Suhaila Yemen

Bibliographic record

VenueTeaching and Teacher Education · 2021
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCreativityMathematics educationPsychologyScience educationPedagogyScience learningSocial psychology

Abstract

fetched live from OpenAlex

This study examines the potential of open-ended student drawing serving as a springboard for creative performance and innovative ways of knowing in undergraduate biology. Our methodology takes into consideration visual stimuli (images on the instructor's PowerPoint slides), students' responses to an open-ended drawing task, and the sociocultural context in which these drawings were produced. Our visual design analysis revealed that nearly 40% of students (n = 20/52) opted for creatively reinventing science examples in a manner that was highly original and novel. It is argued that open-ended drawing can help open the doors of science instruction to student creativity and creative performance.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.089
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.006
Scholarly communication0.0050.005
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.064
GPT teacher head0.438
Teacher spread0.374 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2021
Admission routes1
Has abstractyes

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