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Record W4396762231 · doi:10.1152/advan.00034.2024

Collaborative online international learning in physiology: a case study

2024· article· en· W4396762231 on OpenAlexaff
Jose Ignacio Priego‐Quesada, Andresa M.C. Germano, Daniel Schmidt, Alberto Encarnación‐Martínez, Andressa L. Lemos, Carlos Sendra‐Pérez, Conrado Torres Laett, Emmanuel Souza da Rocha, Falk Zaumseil, Fran Oficial‐Casado, Inaê de Oliveira, Inmaculada Aparicio, Jesus A. Escalona-Navarro, Joaquín Martín Marzano‐Felisatti, Marina Gil-Calvo, Ignacio Catalá-Vilaplana, Niclas Masuch, Pedro Pérez‐Soriano, Roberto Sanchís-Sanchís, Felipe P. Carpes

Bibliographic record

VenueAJP Advances in Physiology Education · 2024
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of British Columbia
FundersSantander Universities
KeywordsMathematics educationComputer scienceMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Although the Collaborative Online International Learning (COIL) methodology is expanding, its use in physiology is still scarce. Our example COIL project of 7 weeks developed a sports-assessment service concept focused on physiology and biomechanics assessments. The program involved teams from Brazil, Germany, and Spain, comprising undergraduate and master's students. Students perceived extracurricular activities in this format as beneficial. Coaches also expressed positive views about such initiatives, noting benefits for students and their development.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.307
Teacher spread0.302 · 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 designQualitative
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

Citations3
Published2024
Admission routes1
Has abstractyes

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