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Record W2787023619 · doi:10.21083/nrsc.v0i11.3998

Concevoir un parcours d’auto-apprentissage guidé de la prononciation du FLE sur Moodle

2018· article· fr· W2787023619 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNouvelle Revue Synergies Canada · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Une façon « décomplexée » d’enseigner la prononciation en classe, basée sur le corps, les émotions et le groupe (Briet; Collige; Rassart, 2014) constitue indéniablement un atout pour apprendre le français. Néanmoins, pour remédier aux difficultés très diverses dans l’acquisition de la prononciation, le présentiel atteint ses limites (Lauret 170).Afin de permettre à chaque étudiant de poursuivre à son rythme les prises de conscience et le travail initiés en classe, une équipe de l’Université de Louvain développe depuis septembre 2016 un parcours d’auto-apprentissage guidé de la prononciation du FLE sur Moodle.Après avoir dressé l’état de la question, l'article met en évidence les besoins des étudiants. Ensuite, les objectifs et les choix didactiques qui guident la conception du parcours en ligne son développés, avec une insistance sur les moyens qui prolongent en ligne la dynamique instaurée en classe. Quelques exemples illustrent le tout.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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