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Record W3087261681 · doi:10.1515/ijnes-2019-0081

Nursing students’ engagement and experiences with virtual reality in an undergraduate bioscience course

2020· article· en· W3087261681 on OpenAlexaff
D. Scott Thompson, Alison P Thompson, Kristen McConnell

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsLakehead University
Fundersnot available
KeywordsVirtual realityStudent engagementConceptualizationMedical educationExploratory researchNurse educationVirtual learning environmentPsychologyMedicineNursingComputer sciencePedagogyHuman–computer interactionSociology

Abstract

fetched live from OpenAlex

Objectives The challenges of bioscience courses such as anatomy and physiology in nursing education are well documented. Virtual reality has recently become accessible and may support student engagement. The purpose of this project was to describe students' engagement and experiences with virtual reality in a first-year nursing course on anatomy, physiology, and health assessment. Methods We used a cross-sectional design and collected both quantitative and qualitative data. The Exploratory Learning Model guided our work. Results Students perceived their engagement to be higher in virtual reality compared to other teaching methods. Their experiences were positive with students reporting that they found it easy to use, it helped their learning, and they recommended more of it. Conclusions Virtual reality is an accessible tool for supporting student engagement. The Exploratory Learning Model is a useful conceptualization for integrating virtual reality into a course. Future research on the relationship between virtual reality and learning achievements is warranted.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.433

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 communication0.0000.001
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 head0.181
GPT teacher head0.511
Teacher spread0.331 · 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 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".

Quick stats

Citations45
Published2020
Admission routes1
Has abstractyes

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