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Taking a Bite out of the Lab Book: Stereoscopic Laboratory Models in Student's Hands

2009· article· en· W3177198463 on OpenAlexaff
Robin Hopkins, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsSession (web analytics)StereoscopyTest (biology)CurriculumPsychologyMedical educationMathematics educationComputer scienceMedicinePedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Decreasing time for anatomical teaching, coupled with a new generation of techno‐savvy students, has encouraged educators to explore new pedagogical approaches and instructional tools. An evolving tool is a stereoscopic 3D anatomical computer model of the head and neck. The objective of this study was to explore its effectiveness as an instructional and laboratory aid. Third year student volunteers (N=74) were randomly assigned to one of three lab groups. All groups studied the muscles of mastication and completed identical learning objectives during a 45‐minute lab. Each group utilized a different laboratory model: Group I‐gross prosections; Group II‐3D stereoscopic computer model; and Group III‐a hybrid model utilizing both resources. Model efficacy was measured with a pre‐post multiple‐choice quiz. We hypothesized that students using the 3D stereoscopic model or gross prosections would yield similar change scores (post‐test minus pre‐test), while students experiencing the hybrid model would have higher change scores. One week following the initial lab session, participants completed a second post‐test and a qualitative questionnaire to gain insights of knowledge retention and participant preferences respectively. Information gathered may help form new curricula direction. Grant Funding Source TSC‐Small Grant on Teaching

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.242

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 integrity0.0000.001
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 head0.013
GPT teacher head0.251
Teacher spread0.237 · 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 designBench or experimental
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
Published2009
Admission routes1
Has abstractyes

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