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More than Meets the Eye: An Interactive 3D Model of the Eye for Enhanced Learning of the Oculomotor System

2012· article· en· W2275067995 on OpenAlexaff
Lauren Allen, Siddhartha Bhattacharyya, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsComprehensionLikert scaleEye movementComputer scienceSet (abstract data type)PsychologyTest (biology)Human–computer interactionArtificial intelligenceDevelopmental psychology

Abstract

fetched live from OpenAlex

The functional anatomy of the oculomotor system is conceptually difficult for novice students. This is problematic given that the extraocular muscles represent a common site of clinical intervention in the treatment of ocular motility disorders and other diseased states. This study was designed to develop a novel, three‐dimensional (3D) model that is paired with an electronic interface, creating an interactive 3D learning module of the human oculomotor system. Development of the 3D model utilized the Visible Human Project (VHP) Female data set and was refined using multiple forms of 3D software. Undergraduate anatomy students will be exposed to the learning module in a randomized pseudo‐crossover design. Quantitative post‐experience testing of the 3D learning module will be compared to equivalent two‐dimensional (2D) learning materials. Qualitative Likert‐scale questionnaires will be used to assess subjective opinions towards the different learning resources. It is hypothesized that the 3D learning module will enhance students’ spatial and functional comprehension on post‐test measures as well as maintain a higher level of interest, when compared to equivalent traditional 2D educational materials. Grant Funding Source : Western Graduate Scholarship

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.314
Teacher spread0.291 · 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
Published2012
Admission routes1
Has abstractyes

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