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Positive Impact of an Interactive 3D Neuroanatomy E‐learning Resource on Students' Spatial Neuroanatomical Knowledge

2018· article· en· W3176056276 on OpenAlexafffund
Lauren Allen, Trinette Wright, Roy Eagleson, Sandrine de Ribaupierre

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeuroanatomyResource (disambiguation)CurriculumVirtual microscopyVirtual realityComputer sciencePsychologyHuman–computer interactionMedicineNeurosciencePathologyPedagogy

Abstract

fetched live from OpenAlex

Neuroanatomy is one of the most challenging topics in anatomy for many novice students, who report it to be the most difficult subject in their anatomy curriculum. A primary reason for the perceived difficulties is the high degree of spatial complexity that exists between structures. The development of interactive three‐dimensional (3D) models in neuroanatomy curricula may provide an effective solution for alleviating difficulties students encountered with two‐dimensional (2D) learning resources, however this has yet to be fully examined in the literature. Interactive 3D and 2D e‐learning resources, as well as a novel virtual syncretion assessment tool, were developed to complement undergraduate anatomy instruction. The virtual syncretion assessment required participants to place neuroanatomical structures in a partial 3D neuroanatomical model. This assessment format allowed for the evaluation of spatial knowledge based on the accuracy of the 3D placement of structures rather than conventional nominal responses. Performance on the virtual syncretion assessment was measured by both the selection of correct structures, and the accuracy of the placement of structures within the partial 3D neuroanatomical model. One hundred seventy‐three participants completed the study, which utilized a cross‐over design to separate participants into two groups. Each group initially completed an anatomy knowledge pretest, followed by access to either the 3D or 2D neuroanatomy e‐learning resource. Participants completed both an anatomy knowledge quiz and virtual syncretion assessment prior to utilizing the second learning resource. A second anatomy knowledge quiz and virtual syncretion assessment were administered following participants' exposure to the second e‐learning resource. Anatomy knowledge quiz scores of participants who initially accessed the 3D learning resource increased significantly more than the students who initially accessed the 2D e‐learning module. Participants who initially utilized the 3D e‐learning resource performed significantly better on the digital syncretion assessment than participants who initially utilized the 2D e‐learning resource. Additionally, participants who accessed the 3D e‐learning resource subsequent to the 2D e‐learning resource significantly improved their performance on the final virtual syncretion assessment. No significant improvement in performance was observed for participants who accessed the 2D e‐learning resource subsequent to the 3D e‐learning resource. Results of this study could be used to inform the effective development and deployment of interactive 3D e‐learning resources to improve neuroanatomy instruction and assessment. Support or Funding Information Social Sciences and Humanities Research Council This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.929
Threshold uncertainty score0.651

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 science0.0010.000
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 head0.007
GPT teacher head0.285
Teacher spread0.279 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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