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Record W3092141574 · doi:10.18162/ritpu-2020-v17n1-09

Physique, téléphone intelligent et technologie d’impression 3D : étude de cas sur la transition numérique dans l’enseignement des sciences

2020· article· fr· W3092141574 on OpenAlexaffvenue
Chris Isaac Larnder, Faïza Nebia, Margaret Livingstone, Shiwei Huang

Bibliographic record

VenueRevue internationale des technologies en pédagogie universitaire · 2020
Typearticle
Languagefr
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsMarianopolis CollegeCégep du Vieux MontréalJohn Abbott College
Fundersnot available
KeywordsHumanitiesArtPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Nous dcrivons les stades du dveloppement technologique dans le cadre d'un projet de quatre ans visant adapter l'enseignement de la physique au collgial aux ralits de l're numrique. Tirant parti de multiples innovations complmentaires, ce projet nous sert d'tude de cas en vue d'acqurir une comprhension plus gnrale du droulement des transitions technologiques dans un cadre pdagogique et, plus particulirement, dans l'enseignement en sciences et en gnie.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

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