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Record W4210921798 · doi:10.3138/cmlr.61.4.491

Comparing Children's Oral Ability in Two ESL Programs

2005· article· en· W4210921798 on OpenAlexvenueno aff
Joanna White, Carolyn E. Turner

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Computer scienceMathematics educationLanguage proficiencySecond languagePsychologyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates performance on oral tasks across two groups of francophone learners with different ESL instruction. It reports on second language (L2) ability gains in intensive instruction and regular instruction. To date, gains in oral proficiency have not been reported in such programs, nor has there been a comparison across two concurrent programs. Six classes were involved: one intensive ESL and one regular Grade 6 ESL class from each of three schools. Data were collected at the beginning and end of their programs. Three tasks were used: Audio-Pal, Story Retell, and Info-Gap. Results indicate that the intensive ESL students gained significantly more than the regular ESL students in their oral ability on the three tasks. Of particular interest in this article are the characteristics of the instruments and the language performance they generated. The three tasks discriminated across the two programs and enabled us to characterize the differences that were observed.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
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 head0.029
GPT teacher head0.256
Teacher spread0.227 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Modern Language Review/ La Revue canadienne des langues vivantesSame topicEFL/ESL Teaching and LearningFrench-language works237,207