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Record W3127894706 · doi:10.7202/1075728ar

THE USE OF GROUP TESTS TO PROMOTE COLLABORATION AND LEARNING: DO THEY WORK?

2021· article· en· W3127894706 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTest (biology)Mathematics educationClass (philosophy)Group (periodic table)PsychologyGroup workMedical educationMultiple choiceComputer scienceMathematicsMedicineSignificant differenceArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

The research was carried out to determine whether the use of group tests for undergraduate science students to augment lecture material in a second-year core course in microbiology would improve the retention of material on a subsequent regular mid-term/final exam. On three separate occasions, the students were asked to complete short multiple-choice tests individually and then were asked to get together in groups of 4 to re-answer the same questions. The discussions they had in the groups improved their individual marks by 10.9% in the first test, 14.5% in the second test and 20.9% in the third test. Overall, the class average was 2.5% better than the previous year. The majority of the students indicated that the group tests improved their understanding and helped them to learn the lecture material.

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)0.010
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
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 head0.419
GPT teacher head0.484
Teacher spread0.065 · 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