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Papertronic Puppets: Teaching STEM and Storytelling Through Creative Construction

2024· article· en· W4407952014 on OpenAlexaff
Sarah Kushner, John Kanji, Paul Dietz, Daniel Wigdor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStorytellingComputer scienceMultimediaNarrativeArtLiterature

Abstract

fetched live from OpenAlex

This innovative practice full paper finds that story-telling through animatronics can offer a combination of creativity and engineering in order to engage students in an inquiry-based approach to STEM learning. Animatronics is the art and science of bringing a story to life through robotic puppetry. Typically reserved for trained engineers with ample resources, animatronics can be an expressive yet inaccessible medium. We aim to allow students to write, create, and perform an animatronic story, learning valuable technical skills along the way. Our study, which took place over the course of eight weeks, investigates the use of our Paper Animatronics Kit in a Grade 2 and Grade 6 classroom, following the progression of their creative writing, animatronic building, and mentoring processes. We present results of our qualitative analysis of student and teacher interviews and group discussions.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.387
Teacher spread0.333 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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