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Record W3107705273 · doi:10.3138/jvme-2020-0006

Using Play-Doh to Enhance the Perceived Learning of Veterinary Medicine

2020· article· en· W3107705273 on OpenAlexvenueno aff
Rachel Stead, Simon Lygo‐Baker, Antônio Augusto Coppi Maciel Ribeiro, Mariana Pereira de Melo

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRecallVisualizationLimit (mathematics)Mathematics educationVeterinary educationPsychologyComputer scienceMedical educationKnowledge managementMedicinePedagogyCognitive psychologyMathematicsArtificial intelligenceCurriculum

Abstract

fetched live from OpenAlex

Teaching anatomy to veterinary students is challenging, and using two-dimensional (2D) representations may limit the opportunity for learners to make the connections required to fully appreciate the complex structures involved and the relationships between them. This research considered the implementation of three-dimensional (3D) modeling using Play-Doh with learners to consider whether they were able to make effective representations that may then support further learning. The evidence from teacher observations and student feedback suggests that, despite some initial hesitation surrounding the use of what some might perceive as a toy in the higher education classroom, the learners believed that the approach allowed improvement in terms of their understanding, knowledge retention and recall. They reported that the approach enabled greater visualization of the structures they were representing. For teachers, the approach has the advantage that the material is cheap, readily available, easily manipulated, can be reused, and needs no sophisticated technology.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
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)0.0020.000

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 head0.522
GPT teacher head0.596
Teacher spread0.074 · 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.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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