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Record W2890473533 · doi:10.1152/advan.00186.2016

Item statistics derived from three-option versions of multiple-choice questions are usually as robust as four- or five-option versions: implications for exam design

2018· article· en· W2890473533 on OpenAlexaboutno aff
Catherine Loudon, Aide Macias-Muñoz

Bibliographic record

VenueAJP Advances in Physiology Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
FundersDivision of Biological InfrastructureUniversity of California, IrvineNational Science Foundation
KeywordsMultiple choicePoint (geometry)Class (philosophy)Quarter (Canadian coin)Mathematics educationPsychologyIndex (typography)StatisticsComputer scienceMathematicsArtificial intelligenceSignificant difference

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.281
metaresearch head score (Gemma)0.736
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.887

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2810.736
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.010
Science and technology studies0.0010.004
Scholarly communication0.0050.006
Open science0.0040.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.116
GPT teacher head0.442
Teacher spread0.327 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations18
Published2018
Admission routes1
Has abstractyes

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