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Record W2903641258 · doi:10.5539/mas.v13n1p57

Investigating Learners' Attitudes Toward Virtual Reality Learning Environments in Embryology Education

2018· article· en· W2903641258 on OpenAlexvenueno aff
Salasabeel F. M. Alfalah, Jannat Falah, Nadia Muhaidat, Mutasem Elfalah, Orwa Falah

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMemorizationCurriculumVariety (cybernetics)VisualizationComputer scienceProcess (computing)Virtual realityMathematics educationRealization (probability)MultimediaPsychologyHuman–computer interactionPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Embryology is taught to undergraduates in a variety of health sciences, largely depending on traditional teaching methods. The human embryo's development is complex and dynamic, and to digest all the related facts, students are always searching for educational tools to enhance their learning experience. Virtual Reality (VR) is a promising technology that aids in the realization of 3 Dimensional (3D) relations of structures by visualization rather than memorization. This paper utilizes a mixed method research consisting of focus group interviews, followed by a questionnaire to evaluate the current teaching methodology, identify shortfalls of traditional learning tools, and determine how VR may enhance the learning process, and the students' acceptance of introduction of this technology into their curriculum. It demonstrates that a VR embryology system would provide a valuable addition to existing educational tools, which is appealing to users, and has the potential to overcome some of the current methods' shortfalls.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.520

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 studies0.0000.001
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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations6
Published2018
Admission routes1
Has abstractyes

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