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An Exploration of L2 Teachers’ Use of Pedagogical Interventions Devised to Draw L2 Learners’ Attention to Form

2011· article· en· W1913483794 on OpenAlexaff
Daphnée Simard, Gladys Jean

Bibliographic record

VenueLanguage Learning · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychological interventionPsychologyGrammarIntervention (counseling)Class (philosophy)Observational studyFocus on formMathematics educationLinguisticsComputer science

Abstract

fetched live from OpenAlex

This descriptive observational study aimed at exploring the form‐focused instruction (FFI) interventions used by four French and four English as‐a‐second‐language high school teachers to draw their students’ attention to form. With the help of an Intervention‐on‐Form(s)‐Observation Scheme (IFOS) developed and tested for this purpose, each FFI intervention observed during 60 hours of video‐recorded class time was coded according to its type (e.g., corrective feedback, explanation, enhancement, form‐oriented exercises) and its related characteristics (e.g., linguistic focus, interactional pattern, source of the intervention). The results show that grammar‐oriented interventions are rather frequent in these second‐language classes and that some differences in preferences of intervention types exist in the two contexts. Although FFI interventions are more frequent in the English classes observed, the overall time spent on FFI is significantly higher in the French classes. The reasons for this as well as details regarding the characteristics of the interventions coded through the use of the IFOS will be discussed.

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)0.003
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.365
GPT teacher head0.381
Teacher spread0.016 · 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

Citations60
Published2011
Admission routes1
Has abstractyes

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