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Enriching Anatomy Learning with Virtual Reality Clinical Scenarios

2022· article· en· W4225418918 on OpenAlexaff
Farah Z. Hasan, Jennifer M. McBride, Josh Mitchell, Deewa Anwarzi, Ranil Sonnadara, Bruce Wainman

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsCompute CanadaUniversity of TorontoMcMaster University
FundersCleveland Clinic Foundation
KeywordsVirtual realityComputer scienceAnatomyMedicineHuman–computer interaction

Abstract

fetched live from OpenAlex

Introduction The use of virtual reality (VR) to simply display anatomical specimens typically fails to exploit the potential of VR to enhance the learning environment. For example, anatomy education normally occurs within the confines of a laboratory which is devoid of relevant context, while VR is uniquely capable of providing a clinically‐relevant space that may enhance the anatomy learning experience. However, it is also possible that an enriched VR environment may inhibit learning, perhaps as a result of increased cognitive load. In general, well‐designed educational materials create schemas and organize the acquired knowledge in a way that promotes deeper learning, retention, and easy retrieval in other settings. Efforts must be made, in instructional design, to reduce extraneous and increase germane cognitive load, but how exactly this is done in VR remains an open question. Objective The purpose of this study is to evaluate the educational efficacy of a VR module which includes a clinical scenario presented in an immersive, contextually‐relevant virtual environment. Hypothesis Although learning in a VR enriched environment with a clinical scenario can increase extraneous cognitive load, we hypothesize that it will ultimately facilitate memorization of anatomical structures, when compared with using 3D‐printed physical models or an interactive, 2D environment. Methods Participants with no prior knowledge of pelvic anatomy will be randomly assigned to one of three groups to learn human pelvic anatomy in an identical module presented in VR, an interactive 2D computer‐based module, or physical environment consisting of 3D‐printed models. Prior to the learning phase, participants will complete the Mental Rotation Test (MRT), as well as the Titmus Fly and Titmus Circles tests to assess spatial visualization ability and stereoscopic vision, respectively. They will then complete a pre‐test assessment where they will be asked to identify anatomical structures labeled on a 3D‐printed model. Participants will then be given 10 minutes to learn and memorize pelvic anatomy using their assigned anatomy modality. This will be followed by a post‐test assessment with another set of labeled structures to identify. Finally, participants will complete a survey to share feedback on the learning experience and will complete the Simulation Task Load Index (SIM‐TLX) questionnaire to assess cognitive load. Conclusion The impact of contextually‐relevant enriched virtual environments and clinical scenarios for VR‐based anatomy education have yet to be explored. The findings from this study will provide valuable insight to inform the design of future VR learning tools that not only reduce or limit factors which can impair learning, but also suggest factors within the learning environment which have the potential to improve anatomy learning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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