MétaCan
Menu
Back to cohort
Record W3192152301 · doi:10.1097/nne.0000000000001065

Motivation and Engagement of Nursing Students in 2 Gamified Courses

2021· article· en· W3192152301 on OpenAlex
Laura A. Killam, Katherine E. Timmermans, Sidney Shapiro

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNurse Educator · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsCambrian College
Fundersnot available
KeywordsStudent engagementPsychologyPerceptionMedical educationNurse educationPedagogyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Evidence suggests that gamification increases student engagement in course activities. However, student feedback about gamification in nursing contexts is needed. PURPOSE: The aim of this study was to describe nursing student perceptions of how gamification impacts student motivation and engagement. METHODS: A convergent mixed-methods study was conducted. Surveys were completed by 58 first-year nursing students. Survey comments and data extracted from the learning management system were also collected. RESULTS: Students generally found gamification valuable, interesting, and enjoyable. Gamification took effort and was perceived as important. Students felt that they had some choice in participation in the gamified activities. CONCLUSIONS: Gamification is a viable strategy for promoting motivation and engagement among first year nursing students. More qualitative research is needed to enable game design refinement through investigation of contextualized student experiences. Refined game design may impact the development of learner skills through a moderating effect on engagement in course activities.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.660

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.419
Teacher spread0.358 · 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