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The value of drawing in anatomical education and its effects on academic performance and retention

2013· article· en· W3170066822 on OpenAlexaff
Sara Marie Rosa

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsQueen's University
Fundersnot available
KeywordsSession (web analytics)PsychologySubject matterCadaveric spasmMedical educationMathematics educationMedicineComputer sciencePedagogyAnatomyCurriculum

Abstract

fetched live from OpenAlex

Considering everyone learns differently, it is imperative that educators broaden their instructional methods to include all types of learners. Drawing is an educational strategy that can enhance observational skills and heighten attention. It was hypothesized that drawing would facilitate the learning of anatomy and would be positively reflected in assessment outcomes and retention. The subject group for this pilot study consisted of ten undergraduate students enrolled in Introductory Human Anatomy, at Queen's University. Participants attended four sessions where they drew anatomical structures using both cadaveric specimens and selected images as a guide. Topics included subject matter not taught in the course: muscles of mastication, Circle of Willis, brachial plexus and components of the knee joint. A multiple choice quiz was administered at the end of each session to assess learning that occurred as a result of drawing and students received average scores of 68%, 88%, 92% and 78% for respective sessions. To evaluate long‐term retention, a memory quiz was administered at completion of the study where participants scored 78% and 58% on the multiple choice and drawing components respectively. Taking into account both quantitative and qualitative data, it can be concluded that drawing is effective in the learning of anatomy and that this approach would be most beneficial if implemented in a laboratory setting.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.211

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 designOther design
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

Citations0
Published2013
Admission routes1
Has abstractyes

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