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Record W3045793247 · doi:10.1080/15391523.2020.1783401

Virtual high-fidelity simulation assessment of teamwork skills: How do students react?

2020· article· en· W3045793247 on OpenAlex

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

VenueJournal of Research on Technology in Education · 2020
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsYork UniversityOntario Tech University
Fundersnot available
KeywordsCLARITYTeamworkPerceptionRealismFidelityStructural equation modelingPsychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

With advances in technology, new innovative methods for evaluating teamwork skills are emerging, however little research has been done into students’ reactions to such innovative assessments in an educational setting. This study investigated the reactions of undergraduate students to a high-fidelity behavioral simulation assessment for teamwork skills and explored some of the factors behind those reactions. 168 undergraduate students completed a simulation assessment and filled out surveys of reactions, perceptions, and personality. The results of a structural equations model indicate that reactions were positively related to perceived scenario realism, characters (chatbots) realism and design clarity.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.204
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.048
GPT teacher head0.509
Teacher spread0.461 · 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