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Record W2038191786 · doi:10.3138/jvme.31.1.45

Informative Evaluation of the Teaching Skills of the Faculty at Alfort Veterinary School

2004· article· en· W2038191786 on OpenAlexvenueno aff
Bernard Toma, Dominique Begon, Jean‐Jacques Fontaine, Dan Rosenberg

Bibliographic record

VenueJournal of Veterinary Medical Education · 2004
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSummative assessmentMedical educationSession (web analytics)Protocol (science)Statement (logic)MedicineVeterinary medicinePsychologyFormative assessmentComputer scienceMathematics educationPathologyAlternative medicine

Abstract

fetched live from OpenAlex

A quantitative method for the informative evaluation of teaching activities was devised using a survey from 33 experts. It is based on an evaluation, by students and two peers, of the four steps of the instructional methodology: analysis of training needs, statement of learning objectives, delivery of teaching session, and testing of student performance. Faculty evaluation is optional, and results are strictly confidential. Results of a three-year trial of this protocol at the Alfort veterinary school are presented and discussed. Each year the method has been assessed and some changes have been implemented. It is now felt that evaluation questionnaires for lectures and tutorial sessions have been validated, while those for laboratories and clinical teaching have to be tested through a larger number of settings. A the same time, a change from informative to summative evaluation is under consideration.

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 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)0.039
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.072
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.382
GPT teacher head0.578
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2004
Admission routes1
Has abstractyes

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