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Record W4289923814 · doi:10.1051/shsconf/202214302005

Les outils numériques d’aide à la révision et à la correction en classe de français : réel levier pour l’apprentissage et l’évaluation de l’orthographe ?

2022· article· fr· W4289923814 on OpenAlexaffabout
Pascal Grégoire

Bibliographic record

VenueSHS Web of Conferences · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsHumanitiesPhilosophyArtPhysics

Abstract

fetched live from OpenAlex

Le numérique mène-t-il les élèves à améliorer leurs performances en écriture lors d’évaluations nationales ? Nous avons testé cette hypothèse auprès de 314 adolescent・e・s québécois・e・s dans une quasi-expérimentation. Trois dispositifs d’écriture numérique (traitement de texte [TT] ; TT et correcticiel [TT+C] ; TT+C et formation) ont été comparés à l’écriture manuscrite. L’analyse quantitative (ANOVA) du nombre d’erreurs montre des gains liés au correcticiel, mais seulement en orthographe (lexicale, grammaticale).

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.523
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.369
Teacher spread0.316 · 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 designNot applicable
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
Published2022
Admission routes2
Has abstractyes

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